Bibliography.bib

@article{Linde2012,
  title={Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition},
  author={Linde, Oskar and Lindeberg, Tony},
  journal={Computer Vision and Image Understanding},
  volume={116},
  number={4},
  pages={538--560},
  year={2012},
  publisher={Elsevier}
}

@inproceedings{Kai2008, 
author={Kai Keng Ang and Zheng Yang Chin and Haihong Zhang and Cuntai Guan}, 
booktitle={2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)}, 
title={Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface}, 
year={2008}, 
volume={}, 
number={}, 
pages={2390-2397}, 
keywords={band-pass filters;brain-computer interfaces;electroencephalography;feature extraction;medical signal processing;filter bank common spatial pattern;motor imagery-based brain computer interfaces;electroencephalogram;EEG;common spatial pattern algorithm;CSP algorithm;spatial filter;classification algorithm;Classification algorithms;Filter bank;Filtering algorithms;Electroencephalography;Band pass filters;Gabor filters;Accuracy}, 
doi={10.1109/IJCNN.2008.4634130}, 
ISSN={2161-4393}, 
month={June},}

@article{Ribet2009, 
author={B. Rivet* and A. Souloumiac and V. Attina and G. Gibert}, 
journal={IEEE Transactions on Biomedical Engineering}, 
title={xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface}, 
year={2009}, 
volume={56}, 
number={8}, 
pages={2035-2043}, 
keywords={Bayes methods;bioelectric potentials;brain-computer interfaces;electroencephalography;medical disorders;medical signal processing;signal classification;spatial filters;unsupervised learning;xDAWN algorithm;evoked potential;brain-computer interface;brain activity;P300 speller BCI paradigm;unsupervised algorithm;spatial filter;EEG signal;Bayesian linear discriminant analysis classifier;neuromuscular disorder;Application software;Brain computer interfaces;Computer interfaces;Electroencephalography;Stochastic processes;Communication system control;Control systems;Spatial filters;Bayesian methods;Linear discriminant analysis;Brain–computer interface (BCI);P300 speller;spatial enhancement;xDAWN algorithm;Adult;Algorithms;Artificial Intelligence;Brain;Electroencephalography;Evoked Potentials;Humans;Male;Man-Machine Systems;Signal Processing, Computer-Assisted}, 
doi={10.1109/TBME.2009.2012869}, 
ISSN={0018-9294}, 
month={Aug},}


@article{vanGerven2009,
title = "Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces",
journal = "Journal of Neuroscience Methods",
volume = "179",
number = "1",
pages = "78 -- 84",
year = "2009",
issn = "0165-0270",
doi = "https://doi.org/10.1016/j.jneumeth.2009.01.016",
url = "http://www.sciencedirect.com/science/article/pii/S0165027009000430",
author = "Marcel van Gerven and Ole Jensen",
keywords = "Brain–computer interface, Covert spatial attention, Support vector machine"
}

@article{Fawcett2006,
title = "An introduction to ROC analysis",
journal = "Pattern Recognition Letters",
volume = "27",
number = "8",
pages = "861--874",
year = "2006",
note = "ROC Analysis in Pattern Recognition",
issn = "0167-8655",
doi = "https://doi.org/10.1016/j.patrec.2005.10.010",
url = "http://www.sciencedirect.com/science/article/pii/S016786550500303X",
author = "Tom Fawcett",
keywords = "ROC analysis, Classifier evaluation, Evaluation metrics"
}

@article{Arico2017, 
author={P. Aricò and G. Borghini and G. Di Flumeri and N. Sciaraffa and A. Colosimo and F. Babiloni}, 
journal={IEEE Transactions on Biomedical Engineering}, 
title={Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends}, 
year={2017}, 
volume={64}, 
number={7}, 
pages={1431-1436}, 
keywords={biomedical electrodes;brain-computer interfaces;electroencephalography;learning (artificial intelligence);medical computing;neurophysiology;operational environments;passive brain-computer interface;pBCI systems;electroencephalography;brain activity;pBCI applications;machine learning technique;neurotechnology;EEG electrodes technology;human machine interaction;Electroencephalography;Real-time systems;Electrocardiography;Machine learning algorithms;Electrooculography;Brain;Adaptive automation;human-machine interaction (HMI);machine learning techniques;mental states;operational environments;passive brain-computer interface (pBCI);Algorithms;Brain Mapping;Brain-Computer Interfaces;Electroencephalography;Equipment Design;Forecasting;Humans;Man-Machine Systems;Pattern Recognition, Automated;Software;Technology Assessment, Biomedical}, 
doi={10.1109/TBME.2017.2694856}, 
ISSN={0018-9294}, 
month={July}
}

@inproceedings{Roy2013, 
author={R. N. Roy and S. Bonnet and S. Charbonnier and A. Campagne}, 
booktitle={2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, 
title={Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI}, 
year={2013}, 
pages={6607--6610}, 
keywords={brain-computer interfaces;cognition;electroencephalography;mental fatigue estimation;working memory load estimation;EEG based passive BCI;mental state monitoring systems;passive brain-computer interfaces;real time assessment;cognitive state;time-on-task;band power features;WKL assessment;alpha power distribution;WKL level discriminability;Electrodes;Fatigue;Electroencephalography;Brain-computer interfaces;Electronic mail;Time-frequency analysis;Accuracy;Adult;Brain-Computer Interfaces;Diagnostic Imaging;Diagnostic Imaging;Electroencephalography;Female;Humans;Male;Memory;Mental Fatigue;Mental Fatigue}, 
doi={10.1109/EMBC.2013.6611070}, 
ISSN={1094-687X}, 
month={July},}


@article{Bashore1991,
title = "Discovery of the P300: A tribute",
journal = "Biological Psychology",
volume = "32",
number = "2",
pages = "155--171",
year = "1991",
issn = "0301-0511",
doi = "https://doi.org/10.1016/0301-0511(91)90007-4",
url = "http://www.sciencedirect.com/science/article/pii/0301051191900074",
author = "Theodore R. Bashore and Maurits W. van der Molen",
keywords = "Samuel Sutton, Guessing task, P300, ERP, late positive component, Stimulus uncertainty, Stimulus probability, Exogenous, Endogenous, Information delivery"
}

@INPROCEEDINGS{Arandjelovic2012,
author = {R. Arandjelovic and A. Zisserman},
booktitle = {2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)},
title = {Three things everyone should know to improve object retrieval},
year = {2012},
pages = {2911--2918},
keywords={image retrieval;retrieval performance;object retrieval;large scale image datasets;image query;image retrieval;video Google;SIFT descriptors;RootSIFT;query expansion;image augmentation method;Vectors;Visualization;Kernel;Standards;Support vector machines;Indexes;Euclidean distance},
doi = {10.1109/CVPR.2012.6248018},
url = {doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248018},
ISSN = {1063-6919},
month={06}
}

@article{Rey-Otero2014,
abstract = {This article presents a detailed description and implementation of the Scale Invariant Feature Transform (SIFT), a popular image matching algorithm. This work contributes to a detailed dissection of SIFT's complex chain of transformations and to a careful presentation of each of its design parameters. A companion online demonstration allows the reader to use SIFT and individually set each parameter to analyze its impact on the algorithm results},
author = {Rey-Otero, I. and Delbracio, M.},
doi = {http://dx.doi.org/10.5201/ipol.2014.82},
issn = {2105-1232},
journal = {Image Processing On Line},
keywords = {feature detection,image comparison,sift},
mendeley-groups = {Thesis},
pages = {370--396},
title = {{Anatomy of the SIFT Method}},
year = {2014}
}


@article{VandenBerg2006,
abstract = {BACKGROUND Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. RESULTS Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. CONCLUSION Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis).In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important.},
archivePrefix = {arXiv},
arxivId = {806598},
author = {van den Berg, Robert A. and Hoefsloot, Huub C J and Westerhuis, Johan A. and Smilde, Age K. and van der Werf, Mari{\"{e}}t J.},
doi = {10.1186/1471-2164-7-142},
eprint = {806598},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/van den Berg et al. - 2006 - Centering, scaling, and transformations improving the biological information content of metabolomics data.pdf:pdf},
isbn = {1471-2164},
issn = {14712164},
journal = {BMC Genomics},
mendeley-groups = {Thesis},
month = {jun},
number = {142},
pages = {15},
pmid = {16762068},
publisher = {BioMed Central},
title = {{Centering, scaling, and transformations: Improving the biological information content of metabolomics data}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16762068 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC1534033},
volume = {7},
year = {2006}
}



@book{Oppenheim2009,
abstract = {This is the standard text for introductory advanced undergraduate and first-year graduate level courses in signal processing. The text gives a coherent and exhaustive treatment of discrete-time linear systems, sampling, filtering and filter design, reconstruction, the discrete-time Fourier and z-transforms, Fourier analysis of signals, the fast Fourier transform, and spectral estimation. The author develops the basic theory independently for each of the transform domains and provides illustrative examples throughout to aid the reader. Discussions of applications in the areas of speech processing, consumer electronics, acoustics, radar, geophysical signal processing, and remote sensing help to place the theory in context. The text assumes a background in advanced calculus, including an introduction to complex variables and a basic familiarity with signals and linear systems theory. If you have this background, the book forms an up-to-date and self-contained introduction to discrete-time signal processing that is appropriate for students and researchers. Discrete-Time Signal Processing also includes an extensive bibliography},
address = {Upper Saddle River, NJ},
author = {Oppenheim, Alan V and Schafer, Ronald W},
isbn = {0137549202},
mendeley-groups = {Thesis},
publisher = {Pearson Education},
title = {{Discrete Time Signal Processing}},
year = {2009}
}



@ARTICLE{Zanini2018, 
author={P. Zanini and M. Congedo and C. Jutten and S. Said and Y. Berthoumieu}, 
journal={IEEE Transactions on Biomedical Engineering}, 
title={Transfer Learning: A Riemannian Geometry Framework With Applications to Brain–Computer Interfaces}, 
year={2018}, 
volume={65}, 
number={5}, 
pages={1107-1116}, 
keywords={brain-computer interfaces;covariance matrices;electroencephalography;Gaussian distribution;geometry;learning (artificial intelligence);medical signal processing;signal classification;cross-subject classification;classifier;spatial covariance matrices;symmetric positive definite matrices;reference covariance matrix;Riemannian Gaussian distributions;classification performances;affine transformation;BCI datasets;BCI transfer learning problem;Riemannian geometry framework;density function;probabilistic classifier;SPD matrices;cross-session classification;EEG-based BCI classification;electroencephalogram-based brain-computer interface classification;Covariance matrices;Manifolds;Symmetric matrices;Geometry;Electroencephalography;Probabilistic logic;Electronic mail;Brain–computer interface (BCI);covariance matrices;electroencephalography (EEG);mixtures of Gaussian;riemannian geometry}, 
doi={10.1109/TBME.2017.2742541}, 
ISSN={0018-9294}, 
month={May}
}

@article{Blankertz2008,
author = {Blankertz, Benjamin and Losch, Florian and Krauledat, Matthias and Dornhege, Guido and Curio, Gabriel and Muller, Klaus-Robert},
doi = {10.1109/TBME.2008.923152},
issn = {0018-9294},
journal = {IEEE Transactions on Biomedical Engineering},
mendeley-groups = {Thesis},
month = {oct},
number = {10},
pages = {2452--2462},
title = {{The Berlin Brain-Computer Interface: Accurate performance from first-session in BCI-naive subjects}},
url = {http://ieeexplore.ieee.org/document/4487097/},
volume = {55},
year = {2008}
}
@article{Pfurtscheller2003,
author = {Pfurtscheller, G. and Neuper, C. and Muller, G.R. and Obermaier, B. and Krausz, G. and Schlogl, A. and Scherer, R. and Graimann, B. and Keinrath, C. and Skliris, D. and Wortz, M. and Supp, G. and Schrank, C.},
doi = {10.1109/TNSRE.2003.814454},
issn = {1534-4320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
mendeley-groups = {Thesis},
month = {jun},
number = {2},
pages = {1--4},
title = {{Graz-BCI: state of the art and clinical applications}},
url = {http://ieeexplore.ieee.org/document/1214714/},
volume = {11},
year = {2003}
}
@article{Neumann2003,
abstract = {Objectives: Direct brain-computer communication uses self regulation of brain potentials to select letters, words, or symbols from a computer menu to re-establish communication in severely paralysed patients. However, not all healthy subjects, or all paralysed patients acquire the skill to self regulate their brain potentials, and predictors of successful learning have not been found yet. Predictors are particularly important, because only successful self regulation will in the end lead to efficient brain-computer communication. This study investigates the question whether initial performance in the self regulation of slow cortical potentials of the brain (SCPs) may be positively correlated to later performance and could thus be used as a predictor. Methods: Five severely paralysed patients diagnosed with amyotrophic lateral sclerosis were trained to produce SCP amplitudes of negative and positive polarity by means of visual feedback and operant conditioning strategies. Performance was measured as percentage of correct SCP amplitude shifts. To determine the relation between initial and later performance in SCP self regulation, Spearman's rank correlations were calculated between maximum and mean performance at the beginning of training (runs 1–30) and mean performance at two later time points (runs 64–93 and 162–191). Results: Spearman's rank correlations revealed a significant relation between maximum and mean performance in runs 1–30 and mean performance in runs 64–93 (r= 0.9 and 1.0) and maximum and mean performance in runs 1–30 and mean performance in runs 162–191 (r=1.0 and 1.0). Conclusions: Initial performance in the self regulation of SCP is positively correlated with later performance in severely paralysed patients, and thus represents a useful predictor for efficient brain-computer communication.},
author = {Neumann, N and Birbaumer, N},
journal = {Journal of Neurology, Neurosurgery {\&}amp; Psychiatry},
mendeley-groups = {Thesis},
month = {aug},
number = {8},
pages = {1117--1121},
title = {{Predictors of successful self control during brain-computer communication}},
url = {http://jnnp.bmj.com/content/74/8/1117.abstract},
volume = {74},
year = {2003}
}
@article{Vaughan2006,
abstract = {The ultimate goal of brain-computer interface (BCI) technology is to provide communication and control capacities to people with severe motor disabilities. BCI research at the Wadsworth Center focuses primarily on noninvasive, electroencephalography (EEG)-based BCI methods. We have shown that people, including those with severe motor disabilities, can learn to use sensorimotor rhythms (SMRs) to move a cursor rapidly and accurately in one or two dimensions. We have also improved P300-based BCI operation. We are now translating this laboratory-proven BCI technology into a system that can be used by severely disabled people in their homes with minimal ongoing technical oversight. To accomplish this, we have: improved our general-purpose BCI software (BCI2000); improved online adaptation and feature translation for SMR-based BCI operation; improved the accuracy and bandwidth of P300-based BCI operation; reduced the complexity of system hardware and software and begun to evaluate home system use in appropriate users. These developments have resulted in prototype systems for every day use in people's homes.},
author = {Vaughan, Theresa M. and McFarland, Dennis J. and Schalk, Gerwin and Sarnacki, William A. and Krusienski, Dean J. and Sellers, Eric W. and Wolpaw, Jonathan R.},
doi = {10.1109/TNSRE.2006.875577},
isbn = {1534-4320},
issn = {15344320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
keywords = {Augmentative communication,Brain-computer interface (BCI),Conditioning,Electroencephalography (EEG),Mu rhythm,P300,Rehabilitation,Sensorimotor cortex},
mendeley-groups = {Thesis},
month = {jun},
number = {2},
pages = {229--233},
pmid = {16792301},
title = {{The wadsworth BCI research and development program: At home with BCI}},
url = {http://ieeexplore.ieee.org/document/1642776/},
volume = {14},
year = {2006}
}



@ARTICLE{Shin2018, 
author={J. Shin and J. Kwon and J. Choi and C. Im}, 
journal={IEEE Access}, 
title={Ternary Near-Infrared Spectroscopy Brain-Computer Interface With Increased Information Transfer Rate Using Prefrontal Hemodynamic Changes During Mental Arithmetic, Breath-Holding, and Idle State}, 
year={2018}, 
volume={6}, 
number={}, 
pages={19491-19498}, 
keywords={bioelectric phenomena;biomedical measurement;brain;brain-computer interfaces;haemodynamics;infrared spectroscopy;medical signal processing;neurophysiology;signal classification;information transfer rate;ternary near-infrared spectroscopy brain-computer interface;breath-holding state;mental arithmetic state;hybrid BCI task;ternary NIRS-BCI;traditional BCI tasks;breathing movements;prefrontal cortex hemodynamic changes;electrophysiological responses;brain hemodynamic responses;near-infrared spectroscopy;multiclass brain-computer interface;prefrontal hemodynamic changes;mental state changes;traditional binary BCI;PFC hemodynamic changes;average offline ternary classification accuracy;idle state;Task analysis;Hemodynamics;Band-pass filters;Spectroscopy;Electroencephalography;Low pass filters;Brain;Brain-computer interfaces;electroencephalography;multi-class classification;near-infrared spectroscopy}, 
doi={10.1109/ACCESS.2018.2822238}, 
ISSN={2169-3536}
}

@ARTICLE{Cruz2018, 
author={A. Cruz and G. Pires and U. J. Nunes}, 
journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, 
title={Double ErrP Detection for Automatic Error Correction in an ERP-Based BCI Speller}, 
year={2018}, 
volume={26}, 
number={1}, 
pages={26-36}, 
keywords={bioelectric potentials;brain-computer interfaces;electroencephalography;feature extraction;medical signal detection;medical signal processing;signal classification;electroencephalogram;tetraplegic participant;feature-level;evoked error potential;P300 classifier;second error detection;double error-related potential detection;P300-based BCI speller;severe motor disability;brain-computer interface;ERP-based BCI speller;automatic error correction;double ErrP detection;Calibration;Reliability;Error correction;Electroencephalography;Standards;Electric potential;Double error-related potentials (ErrP);automatic error correction;brain-computer interface (BCI);P300 ERP;electroencephalogram (EEG);speller}, 
doi={10.1109/TNSRE.2017.2755018}, 
ISSN={1534-4320}, 
month={Jan}
}


@ARTICLE{Cichocki2008, 
author={A. Cichocki and R. Zdunek and S. Amari}, 
journal={IEEE Signal Processing Magazine}, 
title={Nonnegative Matrix and Tensor Factorization [Lecture Notes]}, 
year={2008}, 
volume={25}, 
number={1}, 
pages={142-145}, 
keywords={matrix decomposition;signal processing;tensors;nonnegative matrix factorization;nonnegative tensor factorization;FP-ALS;large-scale problems;IPC methods;QN methods;multilayer structure;multistart initialization conditions;nonnegative components;Tensile stress;Cost function;Matrix decomposition;Clustering algorithms;Data analysis;Brain modeling;Surges;Robustness;Pattern recognition;Image segmentation}, 
doi={10.1109/MSP.2008.4408452}, 
ISSN={1053-5888}
}


@article{Boto2018,
author = {Boto, Elena and Holmes, Niall and Leggett, James and Roberts, Gillian and Shah, Vishal and Meyer, Sofie S and Mu{\~{n}}oz, Leonardo Duque and Mullinger, Karen J and Tierney, Tim M and Bestmann, Sven and Barnes, Gareth R and Bowtell, Richard and Brookes, Matthew J},
journal = {Nature},
mendeley-groups = {Thesis},
month = {mar},
pages = {657},
publisher = {Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
title = {{Moving magnetoencephalography towards real-world applications with a wearable system}},
url = {http://dx.doi.org/10.1038/nature26147 http://10.0.4.14/nature26147 https://www.nature.com/articles/nature26147{\#}supplementary-information},
volume = {555},
year = {2018}
}



@INPROCEEDINGS{Herff2016, 
author={C. Herff and G. Johnson and L. Diener and J. Shih and D. Krusienski and T. Schultz}, 
booktitle={2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, 
title={Towards direct speech synthesis from ECoG: A pilot study}, 
year={2016}, 
volume={}, 
number={}, 
pages={1540-1543}, 
keywords={audio signal processing;bioelectric potentials;brain-computer interfaces;electroencephalography;medical signal processing;signal reconstruction;speech synthesis;direct speech synthesis;ECoG;brain-computer interfaces;BCI;information transfer rates;spelling paradigms;stimulus-evoked potentials;neural activity;natural mode;verbal messages;electrocoticography intracranial activity;temporal areas;audio magnitude spectrogram;audio waveform;reconstructed spectrograms;original spectrograms;temporal waveforms;speech imagery;Spectrogram;Speech;Correlation;Biological system modeling;Image reconstruction;Real-time systems;Brain modeling;Brain-Computer Interfaces;Electroencephalography;Evoked Potentials;Humans;Pilot Projects;Speech}, 
doi={10.1109/EMBC.2016.7591004}, 
ISSN={1558-4615}, 
month={Aug}
}


@incollection{Chaudhary2016,
title = "Chapter 5 - Brain–computer interfaces in the completely locked-in state and chronic stroke",
editor = "Damien Coyle",
series = "Progress in Brain Research",
publisher = "Elsevier",
volume = "228",
pages = "131 - 161",
year = "2016",
booktitle = "Brain-Computer Interfaces: Lab Experiments to Real-World Applications",
issn = "0079-6123",
doi = "https://doi.org/10.1016/bs.pbr.2016.04.019",
url = "http://www.sciencedirect.com/science/article/pii/S0079612316300450",
author = "U. Chaudhary and N. Birbaumer and A. Ramos-Murguialday",
keywords = "Brain–computer interface, Amyotrophic lateral sclerosis, Locked-in state, Complete locked-in state, Stroke, Electroencephalography, Functional near-infrared spectroscopy, Classical conditioning, Communication, Rehabilitation"
}


@book{Dyson1998,
 author = {Dyson, Esther},
 title = {Release 2.1: A Design for Living in the Digital Age},
 year = {1998},
 isbn = {076790012X},
 edition = {1st},
 publisher = {Broadway Books}
} 


@article{Temko2016,
abstract = {It is now generally accepted that EEG is the only reliable way to accurately detect newborn seizures and, as such, prolonged EEG monitoring is increasingly being adopted in neonatal intensive care units. Long EEG recordings may last from several hours to a few days. With neurophysiologists not always available to review the EEG during unsociable hours, there is a pressing need to develop a reliable and robust automatic seizure detection methodda computer algorithm that can take the EEG signal, process it, and output information that supports clinical decision making. In this study, we review existing algorithms based on how the relevant seizure information is exploited. We start with commonly used methods to extract signatures from seizure signals that range from those that mimic the clinical neurophysiologist to those that exploit mathematical models of neonatal EEG generation. Commonly used classification methods are reviewed that are based on a set of rules and thresholds that are either heuristically tuned or automatically derived from the data. These are followed by techniques to use information about spatiotemporal seizure context. The usual errors in system design and validation are discussed. Current clinical decision support tools that have met regulatory requirements and are available to detect neonatal seizures are reviewed with progress and the outstanding challenges are outlined. This review discusses the current state of the art regarding automatic detection of neonatal seizures.},
author = {Temko, Andriy and Lightbody, Gordon},
doi = {10.1097/WNP.0000000000000295},
isbn = {0000000000000},
issn = {15371603},
journal = {Journal of Clinical Neurophysiology},
keywords = {Computer algorithms,Neonatal seizures,Seizure detection},
mendeley-groups = {Thesis},
month = {oct},
number = {5},
pages = {394--402},
pmid = {27749459},
title = {{Detecting neonatal seizures with computer algorithms}},
url = {http://insights.ovid.com/crossref?an=00004691-201610000-00004},
volume = {33},
year = {2016}
}


@article{Fedele2012,
abstract = {Objective: Scalp-derived human somatosensory evoked potentials (SEPs) contain high-frequency oscillations (600. Hz; 'sigma-burst') reflecting concomitant bursts of spike responses in primary somatosensory cortex that repeat regularly at 600. Hz. Notably, recent human intracranial SEP have revealed also 1. kHz responses ('kappa-burst'), possibly reflecting non-rhythmic spiking summed over multiple cells (MUA: multi-unit activity). However, the non-invasive detection of EEG signals at 1. kHz typical for spikes has always been limited by noise contributions from both, amplifier and body/electrode interface. Accordingly, we developed a low-noise recording set-up optimised to map non-invasively 1. kHz SEP components. Methods: SEP were recorded upon 4. Hz left median nerve stimulation in 6 healthy human subjects. Scalp potentials were acquired inside an electrically and magnetically shielded room using low-noise custom-made amplifiers. Furthermore, in order to reduce thermal Johnson noise contributions from the sensor/skin interface, electrode impedances were adjusted to ≤1. k$\Omega$. Responses averaged after repeated presentation of the stimulus (n=. 4000 trials) were evaluated by spatio-temporal pattern analyses in complementary spectral bands. Results: Three distinct spectral components were identified: N20 ({\textless}100. Hz), sigma-burst (450-750. Hz), and kappa-burst (850-1200. Hz). The two high-frequency bursts (sigma, kappa) exhibited distinct and partially independent spatiotemporal evolutions, indicating subcortical as well as several cortical generators. Conclusions: Using a dedicated low-noise set-up, human SEP 'kappa-bursts' at 1. kHz can be non-invasively detected and their scalp distribution be mapped. Their topographies indicate a set of subcortical/cortical generators, at least partially distinct from the topography of the 600. Hz sigma-bursts described previously. Significance: The non-invasive detection and surface mapping of 1. kHz EEG signals presented here provides an essential step towards non-invasive monitoring of multi-unit spike activity. {\textcopyright} 2012 International Federation of Clinical Neurophysiology.},
author = {Fedele, T. and Scheer, H. J. and Waterstraat, G. and Telenczuk, B. and Burghoff, M. and Curio, G.},
doi = {10.1016/j.clinph.2012.04.028},
isbn = {1872-8952 (Electronic)$\backslash$r1388-2457 (Linking)},
issn = {13882457},
journal = {Clinical Neurophysiology},
keywords = {High-frequency EEG,Low-noise EEG acquisition system,Non-invasive electrophysiology,Somatosensory cortex},
mendeley-groups = {Thesis},
month = {dec},
number = {12},
pages = {2370--2376},
pmid = {22710032},
publisher = {Elsevier},
title = {{Towards non-invasive multi-unit spike recordings: Mapping 1kHz EEG signals over human somatosensory cortex}},
url = {https://www.sciencedirect.com/science/article/pii/S1388245712004130},
volume = {123},
year = {2012}
}



@article{Vanhatalo2005,
abstract = {While enormous resources have been recently invested into the development of a variety of neuroimaging techniques, the bandwidth of the clinical EEG, originally set by trivial technical limitations, has remained practically unaltered for over 50 years. An increasing amount of evidence shows that salient EEG signals are observed beyond the bandwidth of the routine clinical EEG, which is typically around 0.5-50 Hz. Physiological and pathological EEG activity ranges at least from 0.01 Hz to several hundred Hz, as demonstrated in recordings of spontaneous activity in the immature human brain, as well as during epileptic seizures, or various kinds of cognitive tasks and states in the adult brain. In the present paper, we will review several arguments leading to the conclusion that elimination of the lower (infraslow) or higher (ultrafast) bands of the EEG frequency spectrum in routine EEG leads to situations where salient and physiologically meaningful features of brain activity are ignored. Recording the full, physiologically relevant range of frequencies is readily attained with commercially available direct-current (DC) coupled amplifiers, which have a wide dynamic range and a high sampling rate. Such amplifiers, combined with appropriate DC-stable electrode-skin interface, provide a genuine full-band EEG (FbEEG). FbEEG is mandatory for a faithful, non-distorted and non-attenuated recording, and it does not have trade-offs that would favor any frequency band at the expense of another. With the currently available electrode, amplifier and data acquisition technology, FbEEG is likely to become the standard approach for a wide range of applications in both basic science and in the clinic. {\textcopyright} 2004 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.},
author = {Vanhatalo, Sampsa and Voipio, Juha and Kaila, Kai},
doi = {10.1016/j.clinph.2004.09.015},
isbn = {1388-2457},
issn = {13882457},
journal = {Clinical Neurophysiology},
keywords = {Broadband EEG,DC-EEG,Epilepsy,Fast oscillations,Neonatal EEG,Sleep,Slow oscillations},
mendeley-groups = {Thesis},
month = {jan},
number = {1},
pages = {1--8},
pmid = {15589176},
publisher = {Elsevier},
title = {{Full-band EEG (FbEEG): An emerging standard in electroencephalography}},
url = {https://www.sciencedirect.com/science/article/pii/S1388245704003748},
volume = {116},
year = {2005}
}


@article{Berger2017,
abstract = {Permutation entropy (PeEn) is a complexity measure that originated
from dynamical systems theory. Specifically engineered to be robustly
applicable to real-world data, the quantity has since been utilised
for a multitude of time series analysis tasks. In electroencephalogram
(EEG) analysis, value changes of PeEn correlate with clinical observations,
among them the onset of epileptic seizures or the loss of consciousness
induced by anaesthetic agents. Regarding this field of application,
the present work suggests a relation between PeEn-based complexity
estimation and spectral methods of EEG analysis: for ordinal patterns
of three consecutive samples, the PeEn of an epoch of EEG appears
to approximate the centroid of its weighted power spectrum. To substantiate
this proposition, a systematic approach based on redundancy reduction
is introduced and applied to sleep and epileptic seizure EEG. The
interrelation demonstrated may aid the interpretation of PeEn in
EEG, and may increase its comparability with other techniques of
EEG analysis.},
author = {Berger, Sebastian and Schneider, Gerhard and Kochs, Eberhard F and Jordan, Denis},
doi = {10.3390/e19120692},
issn = {10994300},
journal = {Entropy},
keywords = {Electroencephalography,Ordinal pattern analysis,Permutation entropy},
mendeley-groups = {Thesis2},
month = {dec},
number = {12},
pages = {1--20},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Permutation entropy: Too complex a measure for EEG time series?}},
url = {http://www.mdpi.com/1099-4300/19/12/692},
volume = {19},
year = {2017}
}
@misc{Blankertz2002,
author = {Blankertz, B},
journal = {BCI Classification Contest November},
mendeley-groups = {Thesis2},
title = {{Documentation second wadsworth {BCI} dataset {P}300 evoked potentials data acquired using {BCI}2000 {P}300 Speller Paradigm}},
year = {2002}
}
@inproceedings{j2018challenge,
author = {Bragg, M},
booktitle = {ACM Symposium on User Interface Software and Technology},
mendeley-groups = {Thesis2},
title = {{The Challenge of Crafting Intelligible Intelligence}},
year = {2018}
}

@article{Brunner2014,
author = {Brunner, Clemens and Blankertz, Benjamin and Cincotti, Febo and K{\"{u}}bler, Andrea and Mattia, Donatella and Miralles, Felip and Nijholt, Anton and Otal, Begonya},
journal = {Lecture Notes in Computer Science},
mendeley-groups = {Thesis2},
number = {1},
pages = {475--486},
title = {{BNCI Horizon 2020 – Towards a Roadmap for Brain / Neural Computer Interaction}},
volume = {8513},
year = {2014}
}

@book{Criminisi2013,
author = {Criminisi, Antonio and Shotton, Jamie},
mendeley-groups = {Thesis2},
publisher = {Springer Science {\&} Business Media},
title = {{Decision forests for computer vision and medical image analysis}},
year = {2013}
}
@incollection{Gasthuis2006,
address = {Milan},
author = {Gasthuis, Kennemer},
booktitle = {Cardiac Arrhythmias 2005},
doi = {10.1007/88-470-0371-7_74},
mendeley-groups = {Thesis2},
pages = {607--608},
publisher = {Springer-Verlag},
title = {{What Are the Benefits of Morphological Signal Analysis Using Digital Technology ?}},
url = {http://link.springer.com/10.1007/88-470-0371-7{\_}74},
year = {2006}
}
@incollection{Gasthuis2006,
address = {Milan},
author = {Gasthuis, Kennemer},
booktitle = {Cardiac Arrhythmias 2005},
doi = {10.1007/88-470-0371-7_74},
mendeley-groups = {Thesis2},
pages = {607--608},
publisher = {Springer-Verlag},
title = {{What Are the Benefits of Morphological Signal Analysis Using Digital Technology ?}},
url = {http://link.springer.com/10.1007/88-470-0371-7{\_}74},
year = {2006}
}
@article{Klein1976,
abstract = {This paper presents an approach to signal waveform analysis in the
frequency range of 1 to 50 Hz. The technique is applicable to any
waveform which is a single valued function of time with continuous
derivatives. The main application of this system has been on the
human EEG. The analyzer, to be described, performs its analysis in
the time domain and abstracts wave shape information from the original
wave and from the successive mathematical derivatives of that wave.
The output from this analyzer consists of six analog descriptors
of the input EEG wave. These descriptors are basic amplitude and
basic frequency of the original input wave, amplitude and frequency
of the first derivative of the input wave, and amplitude and frequency
of the second derivative of the input wave.},
author = {Klein, F},
doi = {10.1109/TBME.1976.324638},
issn = {0018-9294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {diagnosis,electroencephalography,methodology,normal human},
mendeley-groups = {Thesis2},
month = {may},
number = {3},
pages = {246--252},
pmid = {1262036},
title = {{A waveform analyzer applied to the human EEG}},
url = {http://www.embase.com/search/results?subaction=viewrecord{\&}from=export{\&}id=L7176868{\%}5Cnhttp://bj7rx7bn7b.search.serialssolutions.com?sid=EMBASE{\&}issn=00189294{\&}id=doi:{\&}atitle=A+waveform+analyzer+applied+to+the+human+EEG{\&}stitle=IEEE+TRANS.+BIOMED.+ENG.{\&}title=I},
volume = {23},
year = {1976}
}
@book{Louis,
author = {Louis, Erik K St and Frey, Lauren C},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Electroencephalography Introductory.pdf:pdf},
isbn = {9780997975604},
mendeley-groups = {Thesis2},
title = {{Electroencephalography}}
}
@book{Louis,
author = {Louis, Erik K St and Frey, Lauren C},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Electroencephalography Introductory.pdf:pdf},
isbn = {9780997975604},
mendeley-groups = {Thesis2},
title = {{Electroencephalography}},
year = {2016}
}
@article{Owens1984,
author = {Owens, Thomas J and Zandt, George and Taylor, Steven R},
journal = {Journal of Geophysical Research: Solid Earth},
mendeley-groups = {Thesis2},
number = {B9},
pages = {7783--7795},
publisher = {Wiley Online Library},
title = {{Seismic evidence for an ancient rift beneath the Cumberland Plateau, Tennessee: A detailed analysis of broadband teleseismic P waveforms}},
volume = {89},
year = {1984}
}
@article{Picton2006,
author = {Picton, Terence W and Mazaheri, Ali},
doi = {10.1002/0470018860.s00309},
isbn = {0470016191},
issn = {0916-7250},
journal = {Encyclopedia of Cognitive Science},
mendeley-groups = {Thesis2},
number = {5},
pages = {1--4},
title = {{Electroencephalography (EEG)}},
url = {http://doi.wiley.com/10.1002/0470018860.s00309},
year = {2006}
}
@article{Picton2006,
author = {Picton, Terence W and Mazaheri, Ali},
doi = {10.1002/0470018860.s00309},
isbn = {0470016191},
issn = {0916-7250},
journal = {Encyclopedia of Cognitive Science},
mendeley-groups = {Thesis2},
number = {5},
pages = {1--4},
title = {{Electroencephalography (EEG)}},
url = {http://doi.wiley.com/10.1002/0470018860.s00309},
year = {2006}
}

@book{Skoog2000,
author = {Skoog, Douglas A and West, Donald M and Holler, F James and Crouch, Stanley R},
publisher = {Saunders College Publishing},
mendeley-groups = {Thesis2},
title = {{Analytical chemistry: an introduction}},
year = {2000}
}
@article{Stockman1976,
author = {Stockman, G. and Kanal, L and Kyle, M.C.},
journal = {Communications of the ACM},
mendeley-groups = {Thesis2},
number = {12},
pages = {688--695},
publisher = {ACM},
title = {{Structural pattern recognition of carotid pulse waves using a general waveform parsing system}},
volume = {19},
year = {1976}
}
@article{Vareka2012,
abstract = {A correctly preprocessed electroencephalographic signal is necessary
for building successful brain-computer interfaces (BCIs). Since the
evoked responses obtained by stimulation are much weaker than the
continuous EEG signal, the correct signal analysis enhances stimulation-driven
signal components. This paper proposes methods for event-related
potential processing for BCIs based on matching pursuit. The suggested
method is compared with other simple methods which are frequently
used for feature extraction. A multilayer perceptron was used for
classification. The results can be used to improve the feature extraction
for BCI systems. {\&}copy; 2012 IEEE.},
author = {Vareka, L},
doi = {10.1109/TSP.2012.6256347},
isbn = {9781467311182},
journal = {35th International Conference on Telecommunications and Signal Processing, TSP - Proceedings},
keywords = {BCI,electroencephalography,event related potentials,matching pursuit,neuroinformatics},
mendeley-groups = {Thesis2},
number = {2},
pages = {513--516},
title = {{Matching pursuit for {P}300-based brain-computer interfaces}},
year = {2012}
}
@article{Lindeberg2013,
abstract = {A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world. These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space-time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales. It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations. There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space-time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space-time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli. This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.},
archivePrefix = {arXiv},
arxivId = {1701.06333},
author = {Lindeberg, Tony},
doi = {10.1007/s00422-013-0569-z},
eprint = {1701.06333},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Lindeberg - 2013 - A computational theory of visual receptive fields.pdf:pdf},
isbn = {0340-1200},
issn = {03401200},
journal = {Biological Cybernetics},
keywords = {Affine covariance,Complex cell,Double-opponent cell,Functional model,Galilean covariance,Gaussian derivative,Illumination invariance,LGN,Primary visual cortex,Receptive field,Scale covariance,Scale space,Simple cell,Theoretical biology,Theoretical neuroscience,Vision,Visual area V1},
mendeley-groups = {Thesis,Thesis2},
month = {jan},
number = {6},
pages = {589--635},
pmid = {24197240},
title = {{A computational theory of visual receptive fields}},
url = {http://arxiv.org/abs/1701.06333},
volume = {107},
year = {2013}
}
@inproceedings{Dalal2005,
abstract = {We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.},
archivePrefix = {arXiv},
arxivId = {chao-dyn/9411012},
author = {Dalal, Navneet and Triggs, Bill},
booktitle = {Proceedings -  IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005},
doi = {10.1109/CVPR.2005.177},
eprint = {9411012},
isbn = {0769523722},
issn = {1063-6919},
mendeley-groups = {Thesis,Thesis2},
pages = {886--893},
pmid = {9230594},
primaryClass = {chao-dyn},
publisher = {IEEE},
title = {{Histograms of oriented gradients for human detection}},
url = {http://ieeexplore.ieee.org/document/1467360/},
volume = {I},
year = {2005}
}
@article{Haegens2014,
abstract = {Converging electrophysiological evidence suggests that the alpha rhythm plays an important and active role in cognitive processing. Here, we systematically studied variability in posterior alpha peak frequency both between and within subjects. We recorded brain activity using MEG in 51 healthy human subjects under three experimental conditions - rest, passive visual stimulation and an N-back working memory paradigm, using source reconstruction methods to separate alpha activity from parietal and occipital sources. We asked how alpha peak frequency differed within subjects across cognitive conditions and regions of interest, and looked at the distribution of alpha peak frequency between subjects. In both regions we observed an increase of alpha peak frequency from resting state and passive visual stimulation conditions to the N-back paradigm, with a significantly higher alpha peak frequency in the 2-back compared to the 0-back condition. There was a trend for a greater increase in alpha peak frequency during the N-back task in the occipital vs. parietal cortex. The average alpha peak frequency across all subjects, conditions, and regions of interest was 10.3. Hz with a within-subject SD of 0.9. Hz and a between-subject SD of 2.8. Hz. We also measured beta peak frequencies, and except in the parietal cortex during rest, found no indication of a strictly harmonic relationship with alpha peak frequencies. We conclude that alpha peak frequency in posterior regions increases with increasing cognitive demands, and that the alpha rhythm operates across a wider frequency range than the 8-12. Hz band many studies tend to include in their analysis. Thus, using a fixed and limited alpha frequency band might bias results against certain subjects and conditions. {\textcopyright} 2014.},
author = {Haegens, Saskia and Cousijn, Helena and Wallis, George and Harrison, Paul J. and Nobre, Anna C.},
doi = {10.1016/j.neuroimage.2014.01.049},
isbn = {1053-8119},
issn = {10959572},
journal = {NeuroImage},
keywords = {Alpha,Beta,MEG,Oscillations},
mendeley-groups = {Thesis,Thesis2},
month = {may},
pages = {46--55},
pmid = {24508648},
publisher = {Academic Press},
title = {{Inter- and intra-individual variability in alpha peak frequency}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811914000792},
volume = {92},
year = {2014}
}
@article{Basar2012,
abstract = {Aim of the review: Questions related to the genesis and functional correlates of the brain's alpha oscillations around 10. Hz (Alpha) are one of the fundamental research areas in neuroscience. In recent decades, analysis of this activity has been not only the focus of interest for description of sensory-cognitive processes, but has also led to trials for establishing new hypotheses. The present review and the companion review aim to constitute an ensemble of "reasonings and suggestions" to understand alpha oscillations based on a wide range of accumulated findings rather than a trial to launch a new "alpha theory". Surveyed descriptions related to physiology and brain function: The review starts with descriptions of earlier extracellular recordings, field potentials and also considers earlier alpha hypotheses. Analytical descriptions of evoked and event-related responses, event-related desynchronization, the relationship between spontaneous activity and evoked potentials, aging brain, pathology and alpha response in cognitive impairment are in the content of this review. In essence, the gamut of the survey includes a multiplicity of evidence on functional correlates in sensory processing, cognition, memory and vegetative system, including the spinal cord and heart. {\textcopyright} 2012 Elsevier B.V.},
author = {Başar, Erol},
doi = {10.1016/j.ijpsycho.2012.07.002},
isbn = {0167-8760},
issn = {01678760},
journal = {International Journal of Psychophysiology},
keywords = {Aging,Alpha,Child EEG,Cognitive impairment,EEG,Emotion,Event related alpha,Event related coherences,Event related oscillations,Evoked alpha,Evoked coherences,Memory,Pre-stimulus alpha},
mendeley-groups = {Thesis,Thesis2},
month = {oct},
number = {1},
pages = {1--24},
pmid = {22820267},
publisher = {Elsevier},
title = {{A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology}},
url = {https://www.sciencedirect.com/science/article/abs/pii/S0167876012003492},
volume = {86},
year = {2012}
}
@article{Kathner2017,
abstract = {Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13{\%}). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.},
author = {K{\"{a}}thner, Ivo and Halder, Sebastian and Hinterm{\"{u}}ller, Christoph and Espinosa, Arnau and Guger, Christoph and Miralles, Felip and Vargiu, Eloisa and Dauwalder, Stefan and Rafael-Palou, Xavier and Sol{\`{a}}, Marc and Daly, Jean M. and Armstrong, Elaine and Martin, Suzanne and K{\"{u}}bler, Andrea},
doi = {10.3389/fnins.2017.00286},
isbn = {1662-453X},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Assistive technology,Brain-computer interface,EEG,End-user evaluation,Practical electrodes},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pmid = {28588442},
title = {{A multifunctional brain-computer interface intended for home use: An evaluation with healthy participants and potential end users with dry and gel-based electrodes}},
pages={1--17},
year = {2017}
}
@article{Harris1978,
abstract = {This paper makes avaliable a concise review of data windows and their affect on the detection of harmonic signals inthe presence of broard-band noise, and in presence of nearby strong harmonic interference. We also call attention to a number of common errors in the application of windows when used with the fast Fourier transform. This paper includes a comprehensive catalog of data windows along with their significant performance parameters from which the different windows can be compared. Finally, an example demonstrates the use and value of windows to resolve closely apaced harmonic signals characterized by large differences inamplitude.},
author = {Harris, Fredric J.},
doi = {10.1109/PROC.1978.10837},
isbn = {0018-9219},
issn = {15582256},
journal = {Proceedings of the IEEE},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
number = {1},
pages = {51--83},
pmid = {1455106},
title = {{On the Use of Windows for Harmonic Analysis with the Discrete Fourier Transform}},
url = {http://ieeexplore.ieee.org/document/1455106/},
volume = {66},
year = {1978}
}
@article{Rakotomamonjy2008,
author = {Rakotomamonjy, Alain and Guigue, Vincent},
doi = {10.1109/TBME.2008.915728},
issn = {0018-9294},
journal = {IEEE Transactions on Biomedical Engineering},
mendeley-groups = {Thesis,Thesis2},
month = {mar},
number = {3},
pages = {1147--1154},
title = {{BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller}},
url = {http://ieeexplore.ieee.org/document/4454051/},
volume = {55},
year = {2008}
}
@article{Tjepkema-Cloostermans2018,
abstract = {Objective: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promising novel approach, being able to learn from large datasets. Here, we show pilot results of detecting epileptiform discharges using deep neural networks. Methods: We selected 50 EEGs from focal epilepsy patients. All epileptiform discharges (n = 1815) were annotated by an experienced neurophysiologist and extracted as 2 s epochs. In addition, 50 normal EEGs were divided into 2 s epochs. All epochs were divided into a training (n = 41,381) and test (n = 8775) set. We implemented several combinations of convolutional and recurrent neural networks, providing the probability for the presence of epileptiform discharges. The network with the largest area under the ROC curve (AUC) in the test set was validated on seven independent EEGs with focal epileptiform discharges and twelve normal EEGs. Results: The final network had an AUC of 0.94 for the test set. Validation allowed detection of epileptiform discharges with 47.4{\%} sensitivity and 98.0{\%} specificity (FPR: 0.6/min). For the normal EEGs in the validation set, the specificity was 99.9{\%} (FPR: 0.03/min). Conclusions: Deep neural networks can accurately detect epileptiform discharges from scalp EEG recordings. Significance: Deep learning may result in a fundamental shift in clinical EEG analysis.},
author = {Tjepkema-Cloostermans, Marleen C. and de Carvalho, Rafael C.V. and van Putten, Michel J.A.M.},
doi = {10.1016/j.clinph.2018.06.024},
issn = {18728952},
journal = {Clinical Neurophysiology},
keywords = {Convolutional neural networks,Deep learning,EEG,Epilepsy,Epileptiform discharges},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {oct},
number = {10},
pages = {2191--2196},
publisher = {Elsevier},
title = {{Deep learning for detection of focal epileptiform discharges from scalp EEG recordings}},
url = {https://www.sciencedirect.com/science/article/pii/S1388245718311465},
volume = {129},
year = {2018}
}
@article{Kaper2004,
abstract = {We propose an approach to analyze data from the P300 speller paradigm using the machine-learning technique support vector machines. In a conservative classification scheme, we found the correct solution after five repetitions. While the classification within the competition is designed for offline analysis, our approach is also well-suited for a real-world online solution: It is fast, requires only 10 electrode positions and demands only a small amount of preprocessing.},
author = {Kaper, Matthias and Meinicke, Peter and Grossekathoefer, Ulf and Lingner, Thomas and Ritter, Helge},
doi = {10.1109/TBME.2004.826698},
isbn = {0018-9294},
issn = {00189294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {BCI competition 2003,Brain-computer interface,P300 speller,SVM},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {jun},
number = {6},
pages = {1073--1076},
pmid = {15188881},
title = {{BCI competition 2003 - Data set IIb: Support vector machines for the P300 speller paradigm}},
url = {http://ieeexplore.ieee.org/document/1300805/},
volume = {51},
year = {2004}
}
@book{He2013,
abstract = {Human-computer interfaces (HCIs) have become ubiquitous. Interfaces such as keyboards and mouses are used daily while interacting$\backslash$n with computing devices (Ebrahimi et al., 2003). There is a developing need, however, for HCIs that can be used in situations$\backslash$n where these typical interfaces are not viable. Direct brain-computer interfaces (BCI) is a developing field that has been$\backslash$n adding this new dimension of functionality to HCI. BCI has created a novel communication channel, especially for those users$\backslash$n who are unable to generate necessary muscular movements to use typical HCI devices.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {He, Bin},
booktitle = {Neural Engineering: Second Edition},
publisher = {KA-PP},
doi = {10.1007/9781461452270},
eprint = {arXiv:1011.1669v3},
isbn = {9781461452270},
issn = {0090-6964},
pmid = {1000102564},
title = {{Neural engineering: Second edition}},
year = {2013}
}

@article{DaPelo2018,
abstract = {Objective. Event-related potentials (ERPs) are usually obtained by averaging thus neglecting the trial-to-trial latency variability in cognitive electroencephalography (EEG) responses. As a consequence the shape and the peak amplitude of the averaged ERP are smeared and reduced, respectively, when the single-trial latencies show a relevant variability. To date, the majority of the methodologies for single-trial latencies inference are iterative schemes providing suboptimal solutions, the most commonly used being the Woody's algorithm. Approach. In this study, a global approach is developed by introducing a fitness function whose global maximum corresponds to the set of latencies which renders the trial signals most aligned as possible. A suitable genetic algorithm has been implemented to solve the optimization problem, characterized by new genetic operators tailored to the present problem. Main results. The results, on simulated trials, showed that the proposed algorithm performs better than Woody's algorithm in all conditions, at the cost of an increased computational complexity (justified by the improved quality of the solution). Application of the proposed approach on real data trials, resulted in an increased correlation between latencies and reaction times w.r.t. the output from RIDE method. Significance. The above mentioned results on simulated and real data indicate that the proposed method, providing a better estimate of single-trial latencies, will open the way to more accurate study of neural responses as well as to the issue of relating the variability of latencies to the proper cognitive and behavioural correlates.},
author = {{Da Pelo}, P. and {De Tommaso}, M. and Monaco, A. and Stramaglia, S. and Bellotti, R. and Tangaro, S.},
doi = {10.1088/1741-2552/aa9b97},
issn = {17412552},
journal = {Journal of Neural Engineering},
keywords = {EEG,ERP,P300,genetic algorithm,intra-subject variability,latency,single-trial},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {apr},
number = {2},
pages = {026016},
pmid = {29154255},
publisher = {IOP Publishing},
title = {{Trial latencies estimation of event-related potentials in EEG by means of genetic algorithms}},
url = {http://stacks.iop.org/1741-2552/15/i=2/a=026016?key=crossref.edbb5c30c593bdaafc1f43e77f1cc59d},
volume = {15},
year = {2018}
}
@article{Gramfort2013,
abstract = {Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.},
archivePrefix = {arXiv},
arxivId = {[1] A. Gramfort, “MEG and EEG data analysis with MNE-Python,” Front. Neurosci., vol. 7, no. December, pp. 1–13, 2013.},
author = {Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis A. and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and H{\"{a}}m{\"{a}}l{\"{a}}inen, Matti},
doi = {10.3389/fnins.2013.00267},
eprint = {[1] A. Gramfort, “MEG and EEG data analysis with MNE-Python,” Front. Neurosci., vol. 7, no. December, pp. 1–13, 2013.},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Gramfort et al. - 2013 - MEG and EEG data analysis with MNE-Python(2).pdf:pdf},
isbn = {1662-4548 (Print) 1662-453X (Linking)},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Electroencephalography (EEG),Magnetoencephalography (MEG),Neuroimaging,Open-source,Python,Software},
mendeley-groups = {Thesis,Thesis2},
month = {dec},
number = {7},
pages = {267},
pmid = {24431986},
publisher = {Frontiers},
title = {{MEG and EEG data analysis with MNE-Python}},
url = {http://journal.frontiersin.org/article/10.3389/fnins.2013.00267/abstract},
volume = {7},
year = {2013}
}
@book{Freeman2013,
abstract = {The scalp and cortex lie like pages of an open book on which the cortex enciphers vast quantities of information and knowledge. They are recorded and analyzed as temporal and spatial patterns in the electroencephalogram and electrocorticogram. This book describes basic tools and concepts needed to measure and decipher the patterns extracted from the EEG and ECoG.  This book emphasizes the need for single trial analysis using new methods and paradigms, as well as large, high-density spatial arrays of electrodes for pattern sampling. The deciphered patterns reveal neural mechanisms by which brains process sensory information into precepts and concepts. It describes the brain as a thermodynamic system that uses chemical energy to construct knowledge.  The results are intended for use in the search for the neural correlates of intention, attention, perception and learning; in the design of human brain-computer interfaces enabling mental control of machines; and in exploring and explaining the physicochemical foundation of biological intelligence.},
address = {New York, NY},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Freeman, Walter J. and Quiroga, Rodrigo Quian},
booktitle = {Imaging Brain Function With EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals},
doi = {10.1007/978-1-4614-4984-3},
eprint = {arXiv:1011.1669v3},
isbn = {9781461449843},
issn = {1098-6596},
mendeley-groups = {Thesis,Thesis2},
pages = {1--248},
pmid = {25246403},
publisher = {Springer New York},
title = {{Imaging brain function with EEG: Advanced temporal and spatial analysis of electroencephalographic signals}},
url = {http://link.springer.com/10.1007/978-1-4614-4984-3},
year = {2013}
}

@inproceedings{Gu2012,
abstract = {EEG signal classification is a challenging task in that the nature of the EEG data may vary from subject to subject, and change over time for the same subject. To improve classification performance, we propose to construct heterogeneous classifier ensembles, where not only the base classifiers are of different types, but they have different input features as well. The classification performance of the proposed method has been examined on Berlin BCI competition III datasets IVa. Our comparative results clearly show that heterogeneous ensembles outperform single models as well as ensembles having the same input features. {\textcopyright} 2012 IEEE.},
author = {Gu, Shenkai and Jin, Yaochu},
booktitle = {2012 12th UK Workshop on Computational Intelligence, UKCI 2012},
doi = {10.1109/UKCI.2012.6335751},
isbn = {9781467343923},
keywords = {Classifier ensemble,autoregressive,brain-computer interface,common spatial pattern,linear discriminant analysis,support vector machine},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {sep},
pages = {1--8},
publisher = {IEEE},
title = {{Heterogeneous classifier ensembles for EEG-based motor imaginary detection}},
url = {http://ieeexplore.ieee.org/document/6335751/},
year = {2012}
}
@article{Wulsin2011,
abstract = {Clinical electroencephalography (EEG) records vast amounts of human complex data yet is still reviewed primarily by human readers. Deep belief nets (DBNs) are a relatively new type of multi-layer neural network commonly tested on two-dimensional image data but are rarely applied to times-series data such as EEG. We apply DBNs in a semi-supervised paradigm to model EEG waveforms for classification and anomaly detection. DBN performance was comparable to standard classifiers on our EEG dataset, and classification time was found to be 1.7-103.7 times faster than the other high-performing classifiers. We demonstrate how the unsupervised step of DBN learning produces an autoencoder that can naturally be used in anomaly measurement. We compare the use of raw, unprocessed data--a rarity in automated physiological waveform analysis--with hand-chosen features and find that raw data produce comparable classification and better anomaly measurement performance. These results indicate that DBNs and raw data inputs may be more effective for online automated EEG waveform recognition than other common techniques.},
archivePrefix = {arXiv},
arxivId = {036015},
author = {Wulsin, D. F. and Gupta, J. R. and Mani, R. and Blanco, J. A. and Litt, B.},
doi = {10.1088/1741-2560/8/3/036015},
eprint = {036015},
isbn = {1741-2552 (Electronic)$\backslash$r1741-2552 (Linking)},
issn = {17412560},
journal = {Journal of Neural Engineering},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {jun},
number = {3},
pages = {15},
pmid = {21525569},
title = {{Modeling electroencephalography waveforms with semi-supervised deep belief nets: Fast classification and anomaly measurement}},
url = {http://stacks.iop.org/1741-2552/8/i=3/a=036015?key=crossref.0b9a4c710e44d4f17f4f6aa5447f418e},
volume = {8},
year = {2011}
}
@book{Luck2005,
abstract = {Preface Acknowledgments 1 An Introduction to Event-Related Potentials and Their Neural Origins 2 The Design and Interpretation of ERP Experiments 3 Basic Principles of ERP Recording 4 Averaging, Artifact Rejection, and Artifact Correction 5 Filtering 6 Plotting, Measurement, and Analysis 7 ERP Localization 8 Setting Up an ERP Lab Appendix: Basic Principles of Electricity Notes References Index},
archivePrefix = {arXiv},
arxivId = {9780262621960},
author = {Luck, Steven J.},
booktitle = {Monographs of the Society for Research in Child Development},
doi = {10.1118/1.4736938},
eprint = {9780262621960},
isbn = {0262122774},
issn = {1540-5834},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
number = {3},
pages = {388},
pmid = {23782442},
title = {{An Introduction to the Event-Related Potential Technique}},
url = {http://mitpress.mit.edu/sites/default/files/titles/content/9780262621960{\_}sch{\_}0001.pdf{\%}5Cnhttp://cognet.mit.edu/library/books/view?isbn=0262122774{\%}5Cnhttp://www.amazon.com/Introduction-Event-Related-Potential-Technique-Neuroscience/dp/0262621967},
volume = {78},
year = {2005}
}
@article{Buzsaki2012,
abstract = {Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Buzs{\'{a}}ki, Gy{\"{o}}rgy and Anastassiou, Costas A. and Koch, Christof},
doi = {10.1038/nrn3241},
eprint = {NIHMS150003},
isbn = {1471-0048 (Electronic)$\backslash$r1471-003X (Linking)},
issn = {1471003X},
journal = {Nature Reviews Neuroscience},
keywords = {Cellular neuroscience,Computational neuroscience,Extracellular signalling molecules,Ion channels,Synaptic transmission},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
number = {6},
pages = {407--420},
pmid = {22595786},
publisher = {Nature Publishing Group},
title = {{The origin of extracellular fields and currents-EEG, ECoG, LFP and spikes}},
url = {http://www.nature.com/articles/nrn3241},
volume = {13},
year = {2012}
}
@book{Lawrence2010,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Hirsch, Lawrence J. and Richard, Brenner P.},
doi = {10.1017/CBO9781107415324.004},
eprint = {arXiv:1011.1669v3},
file = {:Users/rramele/GoogleDrive/BasicBooks/Atlas-of-EEG-in-Critical-Care.pdf:pdf},
isbn = {9788578110796},
issn = {1098-6596},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pages = {348},
pmid = {25246403},
publisher = {Wiley-Blackwell},
title = {{Atlas of EEG in critical care}},
year = {2010}
}
@book{Kappenman2012,
abstract = {Event-related potentials (ERPs) have been used for decades to study perception, cognition, emotion, neurological and psychiatric disorders, and lifespan development. ERPs consist of multiple components and reflect a specific neurocognitive process. In the past, there was no single source that could be consulted to learn about all the major ERP components; learning about a single ERP component required reading dozens or even hundreds of separate journal articles and book chapters. The Oxford Handbook of Event-Related Potential Components fills this longstanding void with a detailed and comprehensive review of the major ERP components. Comprising 22 chapters by the field's founders and leading researchers, this volume offers extensive coverage of all relevant topics: -the fundamental nature of ERP components, including essential information about how ERP components are defined and isolated -individual components, such as the N170, P300, and ERN -groups of related components within specific research domains, such as language, emotion, and memory -ERP components in special populations, including children, the elderly, nonhuman primates, and patients with neurological disorders, affective disorders, and schizophrenia While undeniably broad in scope, these chapters are accessible to novices while remaining informative and engaging to experts. The Oxford Handbook of Event-Related Potential Components is a unique and valuable resource for students and researchers throughout the brain sciences.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Kappenman, Emily S. and Luck, Steven J.},
booktitle = {The Oxford Handbook of Event-Related Potential Components},
doi = {10.1093/oxfordhb/9780195374148.001.0001},
eprint = {arXiv:1011.1669v3},
isbn = {9780199940356},
issn = {1098-6596},
keywords = {ERP components,ERPs,Emotion,Event-related potentials,Language,Memory,Neurocognitive process},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pmid = {16582340},
title = {{The Oxford Handbook of Event-Related Potential Components}},
year = {2012}
}
@book{Tatum2008,
abstract = {EEG interpretation is a critical skill for most practicing neurologists, as well as for an increasing number of specialists in other practice settings. Handbook of EEG Interpretation, the first illustrated, portable handbook to discuss all aspects of clinical neurophysiology, is an essential means of quick reference for anyone involved in EEG interpretation. Handbook of EEG Interpretation provides practical information on reading EEGs by juxtaposing actual EEGs with bullet points of critical information, making it an essential neurophysiology reference for use during bedside, OR, ER, and ICU EEG interpretation. At once more concise, illustrative, and portable than other texts on EEG, Handbook of EEG Interpretation fits in a labcoat pocket, providing immediate information for anyone involved in EEG interpretation. It is a useful tool for all residents, fellows, and clinicians in neurology as well as many internists, psychiatrists, neurosurgeons, anesthesiologists, ICU staff, ER staff, EEG technologists, and nurses. The book's seven main sections cover normal, abnormal, and epileptiform EEG patterns, as well as seizures, patterns of special significance (e.g., stupor and coma), polysomnography, and neurophysiologic intraoperative monitoring. About the Author},
author = {Tatum, William and Husain, Aatif and Benbadis, Selim and Kaplan, Peter},
booktitle = {Medicine},
doi = {10.1002/2014GB005021},
isbn = {9781933864112},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
publisher = {Demos Medical Publishing},
title = {{Handbook of EEG Interpretation}},
year = {2008}
}
@book{Clerc2016,
abstract = {Specialized. Fields of Application. Brain-Computer Interfaces in Disorders
of Consciousness / Jeremie Mattout, Jacques Luaute, Julien Jung,
Dominique Morlet -- Medical Applications: Neuroprostheses and Neurorehabilitation
/ Laurent Bougrain -- Medical Applications of BCIs for Patient Communication
/ Francois Cabestaing, Louis Mayaud -- BrainTV: Revealing the Neural
Bases of Human Cognition in Real Time / Jean-Philippe Lachaux --
BCIs and Video Games: State of the Art with the OpenViBE2 Project
/ Anatole Lecuyer -- Practical Aspects of BCI Implementation. Analysis
of Patient Need for Computer Interfaces / Louis Mayaud, Salvador
Cabanilles, Eric Azabou -- Sensors: Theory and Innovation / Jean-Michel
Badier, Thomas Lonjaret, Pierre Leleux -- Technical Requirements
for High-quality EEG Acquisition / Emmanuel Maby -- Practical Guide
to Performing an EEG Experiment / Emmanuel Maby -- Step by Step Guide
to BCI Design with OpenViBE. OpenViBE and Other BCI Software Platforms
/ Jussi Lindgren, Anatole Lecuyer -- Illustration of Electrophysiological
Phenomena with OpenViBE / Fabien Lotte, Alison Cellard -- Classification
of Brain Signals with OpenViBE / Laurent Bougrain, Guillaume Serriere
-- OpenViBE Illustration of a P300 Virtual Keyboard / Nathanael Foy,
Theodore Papadopoulo, Maureen Clerc -- Recreational Applications
of OpenViBE: Brain Invaders and Use-the-Force / Anton Andreev, Alexandre
Barachant, Fabien Lotte, Marco Congedo -- Societal Challenges and
Perspectives. Ethical Reflections on Brain-Computer Interfaces /
Florent Bocquelet, Gaelle Piret, Nicolas Aumonier, Blaise Yvert --
Acceptance of Brain-Machine Hybrids: How is Their Brain Perceived
In Vivo? / Bernard Andrieu -- Conclusion and Perspectives / Maureen
Clerc, Laurent Bougrain, Fabien Lotte.},
author = {Clerc, M and Bougrain, L and Lotte, F},
isbn = {9781848219632},
mendeley-groups = {Thesis,Thesis2},
publisher = {ISTE Ltd. and Wiley},
title = {{Brain-computer interfaces, Technology and applications 2(Cognitive Science)}},
year = {2016}
}
@article{c14,
author = {Lowe},
journal = {International journal of computer vision},
mendeley-groups = {Thesis,Thesis2},
number = {2},
pages = {91--110},
publisher = {Springer},
title = {{Distinctive image features from scale-invariant keypoints}},
volume = {60},
year = {2004}
}
@article{c37,
author = {Min and Marzelli and Yoo},
journal = {Trends in biotechnology},
mendeley-groups = {Thesis,Thesis2},
number = {11},
pages = {552--560},
publisher = {Elsevier},
title = {{Neuroimaging-based approaches in the brain--computer interface}},
volume = {28},
year = {2010}
}
@article{Schmidtmann2013,
abstract = {The proportion of signal elements embedded in noise needed to detect a signal is a standard tool for investigating motion perception. This paradigm was applied to the shape domain to determine how local information is pooled into a global percept. Stimulus arrays consisted of oriented Gabor elements that sampled the circumference of concentric radial frequency (RF) patterns. Individual Gabors were oriented tangentially to the shape (signal) or randomly (noise). In different conditions, signal elements were located randomly within the entire array or constrained to fall along one of the concentric contours. Coherence thresholds were measured for RF patterns with various frequencies (number of corners) and amplitudes ("sharpness" of corners). Coherence thresholds (about 10{\%} = 15 elements) were lowest for circular shapes. Manipulating shape frequency or amplitude showed a range where thresholds remain unaffected (frequency ≤ RF4; amplitude ≤ 0.05). Increasing either parameter caused thresholds to rise. Compared to circles, thresholds increased by approximately four times for RF13 and five times for amplitudes of 0.3. Confining the signals to individual contours significantly reduced the number of elements needed to reach threshold (between 4 and 6), independent of the total number of elements on the contour or contour shape. Finally, adding external noise to the orientation of the elements had a greater effect on detection thresholds than adding noise to their position. These results provide evidence for a series of highly sensitive, shape-specific analysers which sum information globally but only from within specific annuli. These global mechanisms are tuned to position and orientation of local elements from which they pool information. The overall performance for arrays of elements can be explained by the sensitivity of multiple, independent concentric shape detectors rather than a single detector integrating information widely across space (e.g. Glass pattern detector).},
author = {Schmidtmann, Gunnar and Gordon, Gael E and Bennett, David M and Loffler, Gunter},
doi = {10.3389/fncom.2013.00037},
journal = {Frontiers in computational neuroscience},
keywords = {contour,form,global shape,signal integration,texture},
mendeley-groups = {Thesis,Thesis2},
pages = {37},
pmid = {23720625},
publisher = {Frontiers Media SA},
title = {{Detecting shapes in noise: tuning characteristics of global shape mechanisms.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23720625 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC3655279},
volume = {7},
year = {2013}
}
@article{wang2004bci,
author = {Wang, Yijun and Zhang, Zhiguang and Li, Yong and Gao, Xiaorong and Gao, Shangkai and Yang, Fusheng},
journal = {Biomedical Engineering, IEEE Transactions on},
mendeley-groups = {Thesis,Thesis2},
number = {6},
pages = {1081--1086},
publisher = {IEEE},
title = {{BCI competition 2003-data set IV: an algorithm based on CSSD and FDA for classifying single-trial EEG}},
volume = {51},
year = {2004}
}
@article{c27,
author = {Hochberg and Serruya and Friehs and Mukand and Saleh and Capla and Branner and Chen and Penn and Donoghue},
journal = {Nature},
mendeley-groups = {Thesis,Thesis2},
number = {7099},
pages = {164--171},
publisher = {Nature Publishing Group},
title = {{Neuronal ensemble control of prosthetic devices by a human with tetraplegia}},
volume = {442},
year = {2006}
}

@incollection{Nijboer2009,
author = {Nijboer, Femke and Broermann, Ursula},
doi = {10.1007/978-3-642-02091-9_11},
mendeley-groups = {Thesis,Thesis2},
booktitle={Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction},
pages = {185--201},
publisher = {Springer Berlin Heidelberg},
title = {{Brain–Computer Interfaces for Communication and Control in Locked-in Patients}},
url = {http://link.springer.com/10.1007/978-3-642-02091-9{\_}11},
year = {2009}
}

@misc{owndataset,
author = {Ramele, R and Villar, A J and Santos, J M},
howpublished = {https://www.kaggle.com/rramele/p300samplingdataset},
url = {https://www.kaggle.com/rramele/p300samplingdataset},
mendeley-groups = {Thesis,Thesis2},
title = {{P300-Dataset RRID:SCR{\_}015977}},
year = {2017}
}
@book{allen2004signal,
author = {Allen, Ronald L and Mills, Duncan},
mendeley-groups = {Thesis,Thesis2},
publisher = {John Wiley {\&} Sons},
title = {{Signal analysis: time, frequency, scale, and structure}},
year = {2004}
}
@article{c51,
author = {Blankertz and Curio and Muller},
journal = {Advances in neural information processing systems},
mendeley-groups = {Thesis,Thesis2},
pages = {157--164},
publisher = {MIT; 1998},
title = {{Classifying single trial EEG: Towards brain computer interfacing}},
volume = {1},
year = {2002}
}
@incollection{c18,
author = {Gentiletti and Tabernig and Acevedo.},
booktitle = {IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health},
doi = {10.1007/978-3-540-74471-9_259},
editor = {M{\"{u}}ller-Karger, Carmen and Wong, Sara and {La Cruz}, Alexandra},
isbn = {978-3-540-74470-2},
keywords = {Electroencephalogram; brain computers interfaces;},
mendeley-groups = {Thesis,Thesis2},
pages = {1117--1121},
publisher = {Springer Berlin Heidelberg},
series = {IFMBE Proceedings},
title = {{Interfaces Cerebro Computadora: Definicion, Tipos y Estado Actual}},
url = {http://dx.doi.org/10.1007/978-3-540-74471-9{\_}259},
volume = {18},
year = {2008}
}

@inproceedings{Jeunet2014,
  title = {{How Well Can We Learn With Standard BCI Training Approaches? A Pilot Study.}},
  author = {Jeunet, Camille and Cellard, Alison and Subramanian, Sriram and Hachet, Martin and N'Kaoua, Bernard and Lotte, Fabien},
  url = {https://hal.inria.fr/hal-01052692},
  booktitle = {{6th International Brain-Computer Interface Conference}},
  address = {Graz, Austria},
  year = {2014},
  month = Sep,
  mendeley-groups = {BCI,BCI2016,Thesis,Crypto Neural Networks,Thesis2},
  keywords = {Brain-Computer Interface (BCI) ; learning ; motor task ; feedback ; training task},
  pdf = {https://hal.inria.fr/hal-01052692/file/BCI_workshop_2014.pdf},
  hal_id = {hal-01052692},
  hal_version = {v1},
}

@article{c56,
author = {Nijboer and Bos, Plass-Oude and Blokland and {van Wijkand Farquhar}},
doi = {10.1080/2326263X.2013.877210},
journal = {Brain-Computer Interfaces},
mendeley-groups = {Thesis,Thesis2},
number = {1},
pages = {50--61},
title = {{Design requirements and potential target users for brain-computer interfaces – recommendations from rehabilitation professionals}},
url = {http://dx.doi.org/10.1080/2326263X.2013.877210},
volume = {1},
year = {2014}
}
@incollection{c55,
author = {Allison, B Z},
booktitle = {Brain-Computer Interfaces},
doi = {10.1007/978-3-642-02091-9_19},
isbn = {978-3-642-02090-2},
mendeley-groups = {Thesis,Thesis2},
pages = {357--387},
publisher = {Springer Berlin Heidelberg},
series = {The Frontiers Collection},
title = {{Toward Ubiquitous BCIs}},
url = {http://dx.doi.org/10.1007/978-3-642-02091-9{\_}19},
year = {2010}
}

@book{Nam2018,
address = {Boca Raton},
doi = {10.1201/9781351231954},
editor = {{Nam, C., Nijholt, A., Lotte, F}},
isbn = {9781351231954},
mendeley-groups = {Thesis,Thesis2},
month = {jan},
pages = {814},
publisher = {CRC Press},
title = {{Brain-Computer Interfaces Handbook}},
url = {https://www.taylorfrancis.com/books/9781351231954},
year = {2018}
}
@article{c29,
author = {Wessberg and Stambaugh and Kralik and Beck and Laubach and Chapin and Kim and Biggs and Srinivasan and Nicolelis},
journal = {Nature},
mendeley-groups = {Thesis,Thesis2},
number = {6810},
pages = {361--365},
publisher = {Nature Publishing Group},
title = {{Real-time prediction of hand trajectory by ensembles of cortical neurons in primates}},
volume = {408},
year = {2000}
}

@incollection{Guger2017,
author = {Guger, Christoph and Allison, Brendan Z and Lebedev, Mikhail A},
booktitle = {Brain Computer Interface Research: A State of the Art Summary 6},
doi = {10.1007/978-3-319-64373-1_1},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Guger, Allison, Lebedev - 2017 - Introduction.pdf:pdf},
mendeley-groups = {Thesis,Thesis2},
pages = {1--8},
publisher = {Springer},
title = {{Brain Computer Interface Research: A State of the Art Summary 6 - Introduction}},
url = {http://link.springer.com/10.1007/978-3-319-64373-1{\_}1},
year = {2017}
}
@article{c52,
author = {Shibasaki and Hallett},
journal = {Clinical Neurophysiology},
mendeley-groups = {Thesis,Thesis2},
number = {11},
pages = {2341--2356},
publisher = {Elsevier},
title = {{What is the Bereitschaftspotential?}},
volume = {117},
year = {2006}
}
@misc{Lotte2007,
abstract = {In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.},
author = {Lotte, F. and Congedo, M. and L{\'{e}}cuyer, A. and Lamarche, F. and Arnaldi, B.},
booktitle = {Journal of Neural Engineering},
doi = {10.1088/1741-2560/4/2/R01},
isbn = {1741-2560},
issn = {17412560},
mendeley-groups = {Thesis,Thesis2},
pmid = {17409472},
title = {{A review of classification algorithms for EEG-based brain-computer interfaces}},
year = {2007}
}
@article{c21,
author = {Duvinage, Matthieu and Castermans, Thierry and Petieau, Mathieu and Hoellinger, Thomas and Cheron, Guy and Dutoit, Thierry},
journal = {Biomedical engineering online},
mendeley-groups = {Thesis,Thesis2},
number = {1},
pages = {56},
publisher = {BioMed Central Ltd},
title = {{Performance of the Emotiv Epoc headset for P300-based applications}},
volume = {12},
year = {2013}
}
@article{Simons2016,
abstract = {Alzheimer's disease (AD) is the most prevalent form of dementia in the world. Symptoms include progressive memory, cognitive and behavioural changes before death, caused by amyloid plaques and hyperphosphorated tau in the brain. The cause of AD is currently unknown and current interventions only slow the decline. Diagnosis is based on patient and familial history, interviews with close family and friends, cognitive, mental and physical tests. The electroencephalogram (EEG) records the electrical signals of the brain, which AD, as a cortical dementia, is known to directly affect. Non-linear signal processing has shown that these changes in the EEG can be identified with complementary findings to linear methods. This thesis aimed to explore these changes with novel univariate and bivariate methods using both synthetic signals and resting, eyes-closed EEGs recorded from 22 subjects, 11 AD patients (MMSE=13.1±5.9 (mean SD)) and 11 age-matched controls (30±0). Permutation entropy showed statistically significant increased complexity in control EEGs for electrodes at the front of the head. Bivariate analysis was novel for this EEG database so coherence was used to create a comparison results set. With the success of Lempel-Ziv Complexity (LZC), distance based bivariate forms were applied (dLZC03). Novel normalisation methods based on that for univariate LZC showed a greater representation of the signal patterns in the results. Volume conduction was shown to significantly impact the results, both of coherence and dLZC03, though this was greater with coherence. Lastly, volume conduction mitigated, bandwidth based pre filtering with dLZC03 was calculated, producing the most significant p values ever recorded with this EEG database. Thus this PhD shows increased distinction between the two subject groups with dLZC03 over LZC and increased distinction with limited but targeted bandwidths from those subject signals.},
author = {Simons, Samantha},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Simons - 2016 - Analysis of brain signals with advanced signal processing techniques to help in the diagnosis of Alzheimer's disease.pdf:pdf},
journal = {PQDT - Global},
keywords = {(UMI)AAI10587731,0541:Biomedical engineering,Alzheimer's disease,Applied sciences,Biomedical engineering,http://epubs.surrey.ac.uk/813461/},
pages={1--8},
mendeley-groups = {Thesis,Thesis2},
title = {{Analysis of brain signals with advanced signal processing techniques to help in the diagnosis of Alzheimer's disease}},
url = {https://search.proquest.com/docview/1883350820?accountid=11440},
year = {2016}
}
@misc{EEGIntro,
author = {Britton, Jeffrey W and Frey, Lauren C and Hopp, Jennifer L and Korb, Pearce and Koubeissi, Mohamad Z and Lievens, William E and Pestana-Knight, Elia M and {St. Louis}, Erik K},
isbn = {9780997975604},
mendeley-groups = {Thesis,Thesis2},
publisher = {American Epilepsy Society, Chicago},
title = {{Electroencephalography ({EEG}): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants}},
url = {http://europepmc.org/books/NBK390354},
year = {2016}
}
@misc{c77,
annote = {US Patent 8,744,588},
author = {Midani and Midani},
mendeley-groups = {Thesis,Thesis2},
publisher = {Google Patents},
title = {{Method and system for connecting an impaired nervous system to a muscle or a group of muscles based on template matching and intelligent end points}},
url = {http://www.google.com/patents/US8744588},
year = {2014}
}
@book{young1970,
author = {Young, Robert Maxwell},
mendeley-groups = {Thesis,Thesis2},
publisher = {Oxford University Press, USA},
title = {{Mind, brain, and adaptation in the nineteenth century: Cerebral localization and its biological context from Gall to Ferrier}},
year = {1970}
}

@article{SellersandEmanuelDonchin2006,
abstract = {We describe current efforts to implement and improve P300-BCI communication tools. The P300 Speller first described by Farwell and Donchin (in 1988) adapted the so-called oddball paradigm (OP) as the operating principle of the brain-computer interface (BCI) and was the first P300-BCI. The system operated by briefly intensifying each row and column of a matrix and the attended row and column elicited a P300 response. This paradigm has been the benchmark in P300-BCI systems, and in the past few years the P300 Speller paradigm has been solidified as a promising communication tool. While promising, we have found that some people who have amyotrophic lateral sclerosis (ALS) would be better suited with a system that has a limited number of choices, particularly if the 6 x 6 matrix is difficult to use. Therefore, we used the OP to implement a four-choice system using the commands: Yes, No, Pass, and End; we also used three presentation modes: auditory, visual, and auditory and visual. We summarize results from both paradigms and also discuss obstacles we have identified while working with the ALS population outside of the laboratory environment.},
author = {Sellers, Eric W and K{\"{u}}bler, Andrea and Donchin, Emanuel},
doi = {10.1109/TNSRE.2006.875580},
isbn = {5184747958},
issn = {15344320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
keywords = {Amyotrophic lateral sclerosis (ALS),Brain-computer interface (BCI),P300},
mendeley-groups = {Thesis,Thesis2},
number = {2},
pages = {221--224},
pmid = {16792299},
title = {{Brain-computer interface research at the University of South Florida cognitive psychophysiology laboratory: The P300 speller}},
volume = {14},
year = {2006}
}

@article{c20,
author = {Park and Jun},
journal = {Expert Systems with Applications},
mendeley-groups = {Thesis,Thesis2},
number = {2},
pages = {3336--3341},
publisher = {Elsevier},
title = {{A simple and fast algorithm for K-medoids clustering}},
volume = {36},
year = {2009}
}
@article{c99,
author = {Iturrate, I and Antelis, J M and Kubler, A and Minguez, J},
doi = {10.1109/TRO.2009.2020347},
issn = {1552-3098},
journal = {Robotics, IEEE Transactions on},
keywords = {brain-computer interfaces;collision avoidance;elec},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
number = {3},
pages = {614--627},
title = {{A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation}},
volume = {25},
year = {2009}
}
@incollection{c98,
author = {Richard and Gentiletti},
booktitle = {IV Latin American Congress on Biomedical Engineering 2007, Bioengineering Solutions for Latin America Health},
doi = {10.1007/978-3-540-74471-9_261},
editor = {M{\"{u}}ller-Karger, Carmen and Wong, Sara and {La Cruz}, Alexandra},
isbn = {978-3-540-74470-2},
keywords = {BCI; P300; Control; Silla de ruedas},
mendeley-groups = {Thesis,Thesis2},
pages = {1127--1130},
publisher = {Springer Berlin Heidelberg},
series = {IFMBE Proceedings},
title = {{Plataforma Experimental de Interfaz Cerebro Computadora Orientada al Control de Sillas de Ruedas}},
url = {http://dx.doi.org/10.1007/978-3-540-74471-9{\_}261},
volume = {18},
year = {2008}
}
@inproceedings{c61,
author = {Sanei and Leyman},
booktitle = {Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on},
doi = {10.1109/SSP.2001.955265},
keywords = {autoregressive processes;bioelectric potentials;bi},
mendeley-groups = {Thesis,Thesis2},
pages = {233--236},
title = {{EEG brain map reconstruction using blind source separation}},
year = {2001}
}
@article{c62,
author = {Jensen, Ole and Bahramisharif, Ali and Oostenveld, Robert and Klanke, Stefan and Hadjipapas, Avgis and Okazaki, Yuka O and van Gerven, Marcel A J},
journal = {Perception Science},
mendeley-groups = {Thesis,Thesis2},
pages = {100},
publisher = {Frontiers},
title = {{Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience}},
volume = {2},
year = {2011}
}
@article{c12,
author = {Liberczuk and Quintero-Rinc{\'{o}}n and Risk},
journal = {IEEE Latin-American Transactions},
mendeley-groups = {Thesis,Thesis2},
number = {126},
publisher = {ARGENCON},
title = {{Evaluaci{\'{o}}n de un mapa Auto-Organizado aplicado a una Interfaz Cerebro Computadora}},
volume = {2012},
year = {2012}
}
@article{Jestico1977,
abstract = {Closed-circuit television and EEG radio-telemetry have been used in the observation of epileptic patients. Since day-time recording sessions last for 5 or 6 h and overnight sessions for considerably longer, the system includes time-lapse video-tape recording to allow accelerated play-back of video-taped material. A method of automatic EEG analysis has been developed so that quantitative studies of sleep, and of epileptic activity during sleep, can be made when the clinical problem demands it. {\textcopyright} 1977.},
author = {Jestico, J. and Fitch, P. and Gilliatt, R. W. and Willison, R. G.},
doi = {10.1016/0013-4694(77)90268-1},
issn = {00134694},
journal = {Electroencephalography and Clinical Neurophysiology},
mendeley-groups = {Thesis,EEGWaveformAnalysis,Thesis2},
month = {sep},
number = {3},
pages = {438--441},
publisher = {Elsevier},
title = {{Automatic and rapid visual analysis of sleep stages and epileptic activity. A preliminary report}},
url = {http://linkinghub.elsevier.com/retrieve/pii/0013469477902681},
volume = {43},
year = {1977}
}
@book{Sanei2007,
abstract = {Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
publisher = {Wiley},
author = {Sanei, Saeid and Chambers, J.A.},
booktitle = {Chemistry {\&} biodiversity},
doi = {10.1002/9780470511923},
eprint = {arXiv:1011.1669v3},
isbn = {9780470511923},
issn = {9780470025819 0470025816},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pmid = {25246403},
title = {{EEG Signal Processing}},
url = {http://doi.wiley.com/10.1002/9780470511923},
year = {2007}
}
@book{Cohen2014,
abstract = {A comprehensive guide to the conceptual, mathematical, and implementational aspects of analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Cohen, Mike X},
publisher = {MIT Press},
doi = {10.1017/CBO9781107415324.004},
eprint = {arXiv:1011.1669v3},
isbn = {0262019876},
issn = {1098-6596},
keywords = {Mike Cohen},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pmid = {25246403},
title = {{Analyzing Neural Time Series Data: Theory and Practice}},
year = {2014}
}
@book{Cacioppo2007,
abstract = {The Handbook of Psychophysiology, 3rd Edition is an essential reference for students, researchers, and professionals in the behavioral, cognitive, and biological sciences. Psychophysiological methods, paradigms, and theories offer entry to a biological cosmos that does not stop at skin's edge, and this essential reference is designed as a road map for explorers of this cosmos. The scope and coverage in the Handbook have expanded to include both a context for and coverage of the biological bases of cognitive, affective, social, and developmental processes and behavior. In addition to updated coverage of the traditional areas of psychophysiology, coverage of the brain and central nervous system has been expanded to include functional neuroimaging, event related brain potentials, electrophysiological source dipole localization, lesion methods, and transcranial magnetic stimulation. It also includes a section on cellular and humoral systems with attention to the communication across and interactions among cellular, immunological, endocrinological, and neural processes.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Cacioppo, John and Tassinary, Louis G and Berntson, Gary G},
booktitle = {Dreaming},
doi = {10.1017/CBO9780511546396},
eprint = {arXiv:1011.1669v3},
isbn = {9780521844710},
issn = {00485772},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pmid = {742301},
title = {{The Handbook of Psychophysiology}},
year = {2007}
}
@book{Vincent2010,
abstract = {1st ed. Edited by the people who were forerunners in creating the field, together with contributions from 34 leading international experts, this handbook provides the definitive reference on Blind Source Separation, giving a broad and comprehensive description of all the core principles and methods, numerical algorithms and major applications in the fields of telecommunications, biomedical engineering and audio, acoustic and speech processing. Going beyond a machine learning perspective, the book reflects recent results in signal processing and numerical analysis, and includes topics such as optimization criteria, mathematical tools, the design of numerical algorithms, convolutive mixtures, and time frequency approaches. This Handbook is an ideal reference for university researchers, R {\&} D engineers and graduates wishing to learn the core principles, methods, algorithms, and applications of Blind Source Separation. Covers the principles and major techniques and methods in one book Edited by the pioneers in the field with contributions from 34 of the world's experts Describes the main existing numerical algorithms and gives practical advice on their design Covers the latest cutting edge topics: second order methods; algebraic identification of under-determined mixtures, time-frequency methods, Bayesian approaches, blind identification under non negativity approaches, semi-blind methods for communications Shows the applications of the methods to key application areas such as telecommunications, biomedical engineering, speech, acoustic, audio and music processing, while also giving a general method for developing applications. Information -- Contrast functions -- Likelihood -- Algebraic methods after prewhitening -- Iterative algorithms -- Second order methods based on color -- Convolutive mixtures -- Algebraic identification of under-determined mixtures -- Sparse component analysis -- Quadratic time-frequency domain methods -- Bayesian approaches -- Non-negative mixtures -- Nonlinear mixtures -- Semi-blind methods for communications -- Overview of source separation applications -- Application to telecommunications -- Biomedical applications -- Audio applications.},
author = {Vincent, E. and Deville, Y.},
booktitle = {English},
doi = {10.1016/B978-0-12-374726-6.00024-2},
isbn = {9780123747266},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pages = {831},
publisher = {Elsevier},
title = {{Handbook of Blind Source Separation - Independent Component Analysis and Applications}},
year = {2010}
}
@article{Gribonval2008,
abstract = {This paper provides new results on computing simultaneous sparse approximations of multichannel signals over redundant dictionaries using two greedy algorithms. The first one, p-thresholding, selects the S atoms that have the largest p-correlation while the second one, p-simultaneous matching pursuit (p-SOMP), is a generalisation of an algorithm studied by Tropp in (Signal Process. 86:572–588, 2006). We first provide exact recovery conditions as well as worst case analyses of all algorithms. The results, expressed using the standard cumulative coherence, are very reminiscent of the single channel case and, in particular, impose stringent restrictions on the dictionary.},
author = {Gribonval, R{\'{e}}mi and Rauhut, Holger and Schnass, Karin and Vandergheynst, Pierre},
doi = {10.1007/s00041-008-9044-y},
isbn = {1531-5851},
issn = {10695869},
journal = {Journal of Fourier Analysis and Applications},
keywords = {Average analysis,Greedy algorithms,Multi-channel,OMP,Thresholding},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {dec},
number = {5-6},
pages = {655--687},
publisher = {SP Birkh{\"{a}}user Verlag Boston},
title = {{Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms}},
url = {http://link.springer.com/10.1007/s00041-008-9044-y},
volume = {14},
year = {2008}
}
@incollection{Gasthuis2006,
address = {Milan},
author = {Gasthuis, Kennemer},
booktitle = {Cardiac Arrhythmias 2005},
doi = {10.1007/88-470-0371-7_74},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Morphological Signal Analysis with Digital Signals.pdf:pdf},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {607--608},
publisher = {Springer-Verlag},
title = {{What Are the Benefits of Morphological Signal Analysis Using Digital Technology ?}},
url = {http://link.springer.com/10.1007/88-470-0371-7{\_}74},
year = {2006}
}
@article{Michel2012,
abstract = {Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. {\textcopyright} 2011 Elsevier Inc.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Michel, Christoph M. and Murray, Micah M.},
doi = {10.1016/j.neuroimage.2011.12.039},
eprint = {NIHMS150003},
isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
keywords = {Brain imaging,Brain mapping,EEG,ERP,Evoked,Frequency,Induced,Multimodal,Source localization,Topography},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {jun},
number = {2},
pages = {371--385},
pmid = {22227136},
publisher = {Academic Press},
title = {{Towards the utilization of EEG as a brain imaging tool}},
url = {https://www.sciencedirect.com/science/article/pii/S1053811911014418},
volume = {61},
year = {2012}
}
@article{Huggins2015,
abstract = {Objectives To identify perceptions among people with spinal cord injury (SCI) of the priorities for brain-computer interface (BCI) applications and design features along with the time investment and risk acceptable to obtain a BCI. Design Survey. Setting Research registry participants surveyed via telephone and BCI usage study participants surveyed in person before BCI use. Participants Convenience sample of people with SCI (N=40), consisting of persons from the registry (n=30) and from the BCI study (n=10). Participants were classified as those with low function (n=24) and those with high function (n=16). Interventions Not applicable. Main Outcome Measures Descriptive statistics of functional independence, living situations and support structures, ratings of importance of different task and design features, and acceptable levels of performance, risk, and time investment. Results BCIs were of interest to 96{\%} of the low-function group. Emergency communication was the top priority task (ranked in the top 2 by 43{\%}). The most important design features were "functions the BCI provides" and "simplicity of BCI setup." Desired performance was 90{\%} accuracy, with standby mode errors no more than once every 4 hours and speeds of more than 20 letters per minute. Dry electrodes were preferred over gel or implanted electrodes (P{\textless}.05). Median acceptable setup time was 10 to 20 minutes, satisfying 65{\%} of participants. Conclusions People with low functional independence resulting from SCI have a strong interest in BCIs. Advances in speed and setup time will be required for BCIs to meet the desired performance. Creating BCI functions appropriate to the needs of those with SCI will be of ultimate importance for BCI acceptance with this population.},
archivePrefix = {arXiv},
arxivId = {NIHMS150003},
author = {Huggins, Jane E. and Moinuddin, Aisha A. and Chiodo, Anthony E. and Wren, Patricia A.},
doi = {10.1016/j.apmr.2014.05.028},
eprint = {NIHMS150003},
isbn = {1748-2968, 1471-180X},
issn = {1532821X},
journal = {Archives of Physical Medicine and Rehabilitation},
keywords = {Assistive technology,Brain-computer interfaces,Rehabilitation,Spinal cord injuries,Surveys},
mendeley-groups = {Thesis,Thesis2},
month = {sep},
number = {3},
pages = {S38--S45},
pmid = {21534845},
publisher = {Taylor {\&} Francis},
title = {{What would brain-computer interface users want: Opinions and priorities of potential users with spinal cord injury}},
url = {http://www.tandfonline.com/doi/full/10.3109/17482968.2011.572978},
volume = {96},
year = {2015}
}
@article{Parra2008,
abstract = {This review summarizes linear spatiotemporal signal analysis methods that derive their power from careful consideration of spatial and temporal features of skull surface potentials. BCIs offer tremendous potential for improving the quality of life for those with severe neurological disabilities. At the same time, it is now possible to use noninvasive systems to improve performance for time-demanding tasks. Signal processing and machine learning are playing a fundamental role in enabling applications of BCI and in many respects, advances in signal processing and computation have helped to lead the way to real utility of noninvasive BCI.},
author = {Parra, Lucas C. and Christoforou, Christoforos and Gerson, Adam D. and Dyrholm, Mads and Luo, An and Wagner, Mark and Philiastides, Marios G. and Sajda, Paul},
doi = {10.1109/MSP.2008.4408447},
isbn = {1053-5888},
issn = {10535888},
journal = {IEEE Signal Processing Magazine},
mendeley-groups = {Thesis,Thesis2},
number = {1},
pages = {107--115},
title = {{Spatiotemporal linear decoding of brain state}},
url = {http://ieeexplore.ieee.org/document/4408447/},
volume = {25},
year = {2008}
}
@article{Wang2018,
abstract = {Objective performance-based outcome measures (OMs) have the potential to provide unbiased and reproducible assessments of limb function. However, very few of these performance-based OMs have been validated for upper limb (UL) prosthesis users. OMs validated in other clinical populations (eg, neurologic or musculoskeletal conditions) could be used to fill gaps in existing performance-based OMs for UL amputees. Additionally, a joint review might reveal consistent gaps across multiple clinical populations. Therefore, the objective of this review was to systematically characterize prominent measures used in both sets of clinical populations with regard to (1) location of task performance around the body, (2) possible grips employed, (3) bilateral versus unilateral task participation, and (4) details of scoring mechanisms. A systematic literature search was conducted in EMBASE, Medline, and Cumulative Index to Nursing and Allied Health electronic databases for variations of the following terms: stroke, musculoskeletal dysfunction, amputation, prosthesis, upper limb, outcome, assessments. Articles were included if they described performance-based OMs developed for disabilities of the UL. Results show most tasks were performed with 1 hand in the space directly in front of the participant. The tip, tripod, and cylindrical grips were most commonly used for the specific tasks. Few measures assessed sensation and movement quality. Overall, several limitations in OMs were identified. The solution to these limitations may be to modify and validate existing measures originally developed for other clinical populations as first steps to more aptly measure prosthesis use while more complete assessments for UL prosthesis users are being developed. Level of Evidence Level III.},
author = {Wang, Sophie and Hsu, C. Janice and Trent, Lauren and Ryan, Tiffany and Kearns, Nathan T. and Civillico, Eugene F. and Kontson, Kimberly L.},
doi = {10.1016/j.pmrj.2018.02.008},
isbn = {1934-1482},
issn = {19341482},
journal = {Pm{\&}R},
mendeley-groups = {Thesis,Thesis2},
month = {feb},
pages = {1--16},
publisher = {Elsevier},
title = {{Evaluation of Performance-Based Outcome Measures for the Upper Limb: A Systematic Review}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1934148218300996},
year = {2018}
}
@article{Lotte2018,
abstract = {OBJECTIVE Most current electroencephalography (EEG)-based brain-computer interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately ten years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. APPROACH We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. MAIN RESULTS We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. SIGNIFICANCE This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.},
author = {Lotte, F. and Bougrain, L. and Cichocki, A. and Clerc, M. and Congedo, M. and Rakotomamonjy, A. and Yger, F.},
doi = {10.1088/1741-2552/aab2f2},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Lotte et al. - 2018 - A review of classification algorithms for EEG-based brain–computer interfaces a 10 year update.pdf:pdf},
isbn = {1741-2560 (Print) 1741-2552 (Linking)},
issn = {17412552},
journal = {Journal of Neural Engineering},
keywords = {Riemannian geometry,brain-computer interfaces,classification,deep learning,electroencephalography,spatial filtering,transfer learning},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
number = {3},
pages = {031005},
pmid = {29488902},
publisher = {IOP Publishing},
title = {{A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update}},
url = {http://stacks.iop.org/1741-2552/15/i=3/a=031005?key=crossref.9cd2b15ab65c8ad34b475584b43dc509},
volume = {15},
year = {2018}
}
@article{Ajiboye2017,
abstract = {Background People with chronic tetraplegia, due to high-cervical spinal cord injury, can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as functional electrical stimulation (FES). Users typically command FES systems through other preserved, but unrelated and limited in number, volitional movements (eg, facial muscle activity, head movements, shoulder shrugs). We report the findings of an individual with traumatic high-cervical spinal cord injury who coordinated reaching and grasping movements using his own paralysed arm and hand, reanimated through implanted FES, and commanded using his own cortical signals through an intracortical brain–computer interface (iBCI). Methods We recruited a participant into the BrainGate2 clinical trial, an ongoing study that obtains safety information regarding an intracortical neural interface device, and investigates the feasibility of people with tetraplegia controlling assistive devices using their cortical signals. Surgical procedures were performed at University Hospitals Cleveland Medical Center (Cleveland, OH, USA). Study procedures and data analyses were performed at Case Western Reserve University (Cleveland, OH, USA) and the US Department of Veterans Affairs, Louis Stokes Cleveland Veterans Affairs Medical Center (Cleveland, OH, USA). The study participant was a 53-year-old man with a spinal cord injury (cervical level 4, American Spinal Injury Association Impairment Scale category A). He received two intracortical microelectrode arrays in the hand area of his motor cortex, and 4 months and 9 months later received a total of 36 implanted percutaneous electrodes in his right upper and lower arm to electrically stimulate his hand, elbow, and shoulder muscles. The participant used a motorised mobile arm support for gravitational assistance and to provide humeral abduction and adduction under cortical control. We assessed the participant's ability to cortically command his paralysed arm to perform simple single-joint arm and hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralysed arm with that of a virtual three-dimensional arm. This study is registered with ClinicalTrials.gov, number NCT00912041. Findings The intracortical implant occurred on Dec 1, 2014, and we are continuing to study the participant. The last session included in this report was Nov 7, 2016. The point-to-point target acquisition sessions began on Oct 8, 2015 (311 days after implant). The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80–100{\%} accuracy), using first a virtual arm and second his own arm animated by FES. Using his paralysed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session 463 days after implant) and feed himself (717 days after implant). Interpretation To our knowledge, this is the first report of a combined implanted FES+iBCI neuroprosthesis for restoring both reaching and grasping movements to people with chronic tetraplegia due to spinal cord injury, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoration of reaching and grasping after paralysis. Funding National Institutes of Health, Department of Veterans Affairs.},
archivePrefix = {arXiv},
arxivId = {15334406},
author = {Ajiboye, A. Bolu and Willett, Francis R. and Young, Daniel R. and Memberg, William D. and Murphy, Brian A. and Miller, Jonathan P. and Walter, Benjamin L. and Sweet, Jennifer A. and Hoyen, Harry A. and Keith, Michael W. and Peckham, P. Hunter and Simeral, John D. and Donoghue, John P. and Hochberg, Leigh R. and Kirsch, Robert F.},
doi = {10.1016/S0140-6736(17)30601-3},
eprint = {15334406},
isbn = {0140-6736},
issn = {1474547X},
journal = {The Lancet},
mendeley-groups = {Thesis,Thesis2},
month = {may},
number = {10081},
pages = {1821--1830},
pmid = {28363483},
publisher = {Elsevier},
title = {{Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration}},
url = {https://www.sciencedirect.com/science/article/pii/S0140673617306013},
volume = {389},
year = {2017}
}
@article{Huebner2018,
author = {Huebner, David and Verhoeven, Thibault and Mueller, Klaus-Robert and Kindermans, Pieter-Jan and Tangermann, Michael},
doi = {10.1109/MCI.2018.2807039},
issn = {1556-603X},
journal = {IEEE Computational Intelligence Magazine},
mendeley-groups = {Thesis,Thesis2},
month = {may},
number = {2},
pages = {66--67},
title = {{Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]}},
url = {http://ieeexplore.ieee.org/document/8335845/},
volume = {13},
year = {2018}
}
@article{Yu2011,
abstract = {Searching for target images in large volume imagery is a challenging problem and the rapid serial visual presentation (RSVP) triage is potentially a promising solution to the problem. RSVP triage is essentially a cortically-coupled computer vision technique that relies on single-trial detection of event-related potentials (ERP). In RSVP triage, images are shown to a subject in a rapid serial sequence. When a target image is seen by the subject, unique ERP characterized by P300 are elicited. Thus, in RSVP triage, accurate detection of such distinct ERP allows for fast searching of target images in large volume imagery. The accuracy of the distinct ERP detection in RSVP triage depends on the feature extraction method, for which the common spatial pattern analysis (CSP) was used with limited success. This paper presents a novel feature extraction method, termed common spatio-temporal pattern (CSTP), which is critical for robust single-trial detection of ERP. Unlike the conventional CSP, whereby only spatial patterns of ERP are considered, the present proposed method exploits spatial and temporal patterns of ERP separately, providing complementary spatial and temporal features for high accurate single-trial ERP detection. Numerical study using data collected from 20 subjects in RSVP triage experiments demonstrates that the proposed method offers significant performance improvement over the conventional CSP method (corrected p-value {\textless} 0.05, Pearson r = 0.64) and other competing methods in the literature. This paper further shows that the main idea of CSTP can be easily applied to other methods.},
author = {Yu, Ke and Shen, Kaiquan and Shao, Shiyun and Ng, Wu Chun and Kwok, Kenneth and Li, Xiaoping},
doi = {10.1109/TBME.2011.2158542},
issn = {00189294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Common spatio-temporal pattern,rapid serial visual presentation triage,single trial},
mendeley-groups = {Thesis,Thesis2},
month = {sep},
number = {9},
pages = {2513--2520},
pmid = {21642038},
title = {{Common spatio-temporal pattern for single-trial detection of event-related potential in rapid serial visual presentation triage}},
url = {http://ieeexplore.ieee.org/document/5783508/},
volume = {58},
year = {2011}
}
@article{Candes2009,
abstract = {This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.},
archivePrefix = {arXiv},
arxivId = {0912.3599},
author = {Candes, Emmanuel J. and Li, Xiaodong and Ma, Yi and Wright, John},
doi = {10.1145/1970392.1970395},
eprint = {0912.3599},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Cand{\`{e}}s et al. - 2011 - Robust principal component analysis.pdf:pdf},
isbn = {0780362780},
issn = {0899-7667},
journal = {Journal of the ACM},
keywords = {Principal components,duality,low-rank matrices,nuclear-norm minimization,robustness vis-a-vis outliers,sparsity,video surveillance,ℓ 1 -norm minimization},
mendeley-groups = {Thesis,Thesis2},
month = {may},
number = {3},
pages = {1--37},
pmid = {19686071},
publisher = {ACM},
title = {{Robust Principal Component Analysis?}},
url = {http://arxiv.org/abs/0912.3599},
volume = {58},
year = {2009}
}
@article{Martin2018,
abstract = {Certain brain disorders resulting from brainstem infarcts, traumatic brain injury, cerebral palsy, stroke and amyotrophic lateral sclerosis, limit verbal communication despite the patient being fully aware. People that cannot communicate due to neurological disorders would benefit from a system that can infer internal speech directly from brain signals. In this review article, we describe the state of the art in decoding inner speech, ranging from early acoustic sound features, to higher order speech units. We focused on intracranial recordings, as this technique allows monitoring brain activity with high spatial, temporal, and spectral resolution, and therefore is a good candidate to investigate inner speech. Despite intense efforts, investigating how the human cortex encodes inner speech remains an elusive challenge, due to the lack of behavioral and observable measures. We emphasize various challenges commonly encountered when investigating inner speech, and propose potential solutions in order to get closer to a natural speech assistive device.},
author = {Martin, Stephanie and Iturrate, I{\~{n}}aki and Mill{\'{a}}n, Jos{\'{e}} del R. and Knight, Robert T. and Pasley, Brian N.},
doi = {10.3389/fnins.2018.00422},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Martin et al. - 2018 - Decoding Inner Speech Using Electrocorticography Progress and Challenges Toward a Speech Prosthesis.pdf:pdf},
issn = {1662-453X},
journal = {Frontiers in Neuroscience},
keywords = {Brain-computer interface,Decoding,electrocorticography,inner speech,neuroprosthetics},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
pages = {422},
publisher = {Frontiers},
title = {{Decoding Inner Speech Using Electrocorticography: Progress and Challenges Toward a Speech Prosthesis}},
url = {https://www.frontiersin.org/article/10.3389/fnins.2018.00422/full},
volume = {12},
year = {2018}
}
@article{Annen2018,
abstract = {Detection and interpretation of signs of ‘covert command following' in patients with disorders of consciousness remains a challenge for clinicians. In this study, we used a tactile P3-based BCI in 12 patients without behavioral command following, attempting to establish ‘covert command following'. These results were then confronted to cerebral metabolism preservation as measured with glucose PET (FDG-PET). One patient showed ‘covert command following' (i.e., above-threshold BCI performance) during the active tactile paradigm. This patient also showed a higher cerebral glucose metabolism within the language network (presumably required for command following) when compared with the other patients without ‘covert command-following' but having a cerebral glucose metabolism indicative of minimally conscious state. Our results suggest that the P3-based BCI might probe ‘covert command following' in patients without behavioral response to command and therefore could be a valuable addition in the clinical assessment of patients with disorders of consciousness.},
author = {Annen, Jitka and Blandiaux, S{\'{e}}verine and Lejeune, Nicolas and Bahri, Mohamed A. and Thibaut, Aurore and Cho, Woosang and Guger, Christoph and Chatelle, Camille and Laureys, Steven},
doi = {10.3389/fnins.2018.00370},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Annen et al. - 2018 - BCI Performance and Brain Metabolism Profile in Severely Brain-Injured Patients Without Response to Command at Bed.pdf:pdf},
issn = {1662453X},
journal = {Frontiers in Neuroscience},
keywords = {Brain computer interface,Consciousness,Covert command following,Disorders of consciousness,FDG-PET,P3},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
number = {6},
pages = {370},
pmid = {29910708},
publisher = {Frontiers},
title = {{BCI performance and brain metabolism profile in severely brain-injured patients without response to command at bedside}},
url = {https://www.frontiersin.org/article/10.3389/fnins.2018.00370/full},
volume = {12},
year = {2018}
}
@article{Ang2011,
abstract = {Brain-computer interface (BCI) technology has the prospects of helping stroke survivors by enabling the interaction with their environ ment through brain signals rather than through muscles, and restoring motor function by inducing activity-dependent brain plasticity. This paper presents a clinical study on the extent of detectable brain signals from a large population of stroke patients in using EEG-based motor imagery BCI. EEG data were collected from 54 stroke patients whereby finger tapping and motor imagery of the stroke-affected hand were performed by 8 and 46 patients, respectively. EEG data from 11 patients who gave further consent to perform motor imagery were also collected for second calibration and third independent test sessions conducted on separate days. Off-line accuracies of classifying the two classes of EEG from finger tapping or motor imagery of the stroke-affected hand versus the EEG from background rest were then assessed and compared to 16 healthy subjects. The mean off-line accuracy of detecting motor imagery by the 46 patients (mu=0.74) was significantly lower than finger tapping by 8 patients (mu=0.87, p=0.008), but not significantly lower than motor imagery by healthy subjects (mu=0.78, p=0.23). Six stroke patients performed motor imagery at chance level, and no correlation was found between the accuracies of detecting motor imagery and their motor impairment in terms of Fugl-Meyer Assessment (p=0.29). The off-line accuracies of the 11 patients in the second session (mu=0.76) were not significantly different from the first session (mu=0.72, p=0.16), or from the on-line accuracies of the third independent test session (mu=0.82, p=0.14). Hence this study showed that the majority of stroke patients could use EEG-based motor imagery BCI.},
author = {Ang, Kai Keng and Guan, Cuntai and Chua, Karen Sui Geok and Ang, Beng Ti and Kuah, Christopher Wee Keong and Wang, Chuanchu and Phua, Kok Soon and Chin, Zheng Yang and Zhang, Haihong},
doi = {10.1177/155005941104200411},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Ang et al. - 2011 - A Large Clinical Study on the Ability of Stroke Patients to Use an EEG-Based Motor Imagery Brain-Computer Interface.pdf:pdf},
isbn = {10.1177/155005941104200411},
issn = {15500594},
journal = {Clinical EEG and Neuroscience},
keywords = {Brain-computer interface,Electroencephalography,Motor imagery,Stroke rehabilitation},
mendeley-groups = {Thesis,Thesis2},
month = {oct},
number = {4},
pages = {253--258},
pmid = {22208123},
publisher = {SAGE PublicationsSage CA: Los Angeles, CA},
title = {{A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface}},
url = {http://journals.sagepub.com/doi/10.1177/155005941104200411},
volume = {42},
year = {2011}
}
@article{Pandarinath2017,
abstract = {{\textless}p{\textgreater}Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Sp{\"{u}}ler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O'Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4–4.2) and information throughput (by a factor of 2.2–4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function.{\textless}/p{\textgreater}},
author = {Pandarinath, Chethan and Nuyujukian, Paul and Blabe, Christine H. and Sorice, Brittany L. and Saab, Jad and Willett, Francis R. and Hochberg, Leigh R. and Shenoy, Krishna V. and Henderson, Jaimie M.},
doi = {10.7554/eLife.18554},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Pandarinath et al. - 2017 - High performance communication by people with paralysis using an intracortical brain-computer interface.pdf:pdf},
isbn = {2050-084X (Electronic)
2050-084X (Linking)},
issn = {2050084X},
journal = {eLife},
keywords = {ALS,assistive technology,brain-machine interface,neural prosthesis},
mendeley-groups = {Thesis,Thesis2},
month = {feb},
pages = {1--12},
pmid = {28220753},
publisher = {eLife Sciences Publications Limited},
title = {{High performance communication by people with paralysis using an intracortical brain-computer interface}},
url = {https://elifesciences.org/articles/18554},
volume = {6},
year = {2017}
}
@book{Williams2003,
abstract = {The present thesis can take its place among the numerous doctoral theses and other publications that are currently revolutionizing the area of machine learning . The author{\&}apos;s basic concern is with kernel{\{}based methods and in particular Support Vector algorithms for regression ...{\}}},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Williams, Christopher K. I},
booktitle = {Journal of the American Statistical Association},
doi = {10.1198/jasa.2003.s269},
eprint = {arXiv:1011.1669v3},
isbn = {0262194759},
issn = {0162-1459},
mendeley-groups = {Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {462},
pages = {489--489},
pmid = {21269608},
publisher = {MIT Press},
title = {{Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond}},
url = {http://www.tandfonline.com/doi/abs/10.1198/jasa.2003.s269},
volume = {98},
year = {2003}
}
@article{Zanin2012,
abstract = {Entropy is a powerful tool for the analysis of time series, as it allows describing the probability distributions of the possible state of a system, and therefore the information encoded in it. Nevertheless, important information may be codified also in the temporal dynamics, an aspect which is not usually taken into account. The idea of calculating entropy based on permutation patterns (that is, permutations defined by the order relations among values of a time series) has received a lot of attention in the last years, especially for the understanding of complex and chaotic systems. Permutation entropy directly accounts for the temporal information contained in the time series; furthermore, it has the quality of simplicity, robustness and very low computational cost. To celebrate the tenth anniversary of the original work, here we analyze the theoretical foundations of the permutation entropy, as well as the main recent applications to the analysis of economical markets and to the understanding of biomedical systems.},
author = {Zanin, Massimiliano and Zunino, Luciano and Rosso, Osvaldo A. and Papo, David},
doi = {10.3390/e14081553},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Zanin et al. - 2012 - Permutation Entropy and Its Main Biomedical and Econophysics Applications A Review.pdf:pdf},
isbn = {1099-4300},
issn = {10994300},
journal = {Entropy},
keywords = {EEG,Econophysics,Epilepsy,Forbidden patterns,Permutation entropy,Shannon entropy},
mendeley-groups = {EEGWaveformAnalysis,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {aug},
number = {8},
pages = {1553--1577},
publisher = {Molecular Diversity Preservation International},
title = {{Permutation entropy and its main biomedical and econophysics applications: A review}},
url = {http://www.mdpi.com/1099-4300/14/8/1553},
volume = {14},
year = {2012}
}
@article{Perdikis2014,
abstract = {OBJECTIVE: While brain-computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. APPROACH: This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. MAIN RESULTS: We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human-computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. SIGNIFICANCE: This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80{\%} minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.},
author = {Perdikis, S. and Leeb, R. and Williamson, J. and Ramsay, A. and Tavella, M. and Desideri, L. and Hoogerwerf, E. J. and Al-Khodairy, A. and Murray-Smith, R. and Mill{\'{a}}n, J. D R},
doi = {10.1088/1741-2560/11/3/036003},
issn = {17412552},
journal = {Journal of Neural Engineering},
keywords = {Brain-computer interface,Clinical evaluation,Hybrid BCI,Motor imagery,Text-speller},
mendeley-groups = {Thesis,Thesis2},
month = {jun},
number = {3},
pages = {12},
pmid = {24737114},
publisher = {IOP Publishing},
title = {{Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller}},
url = {http://stacks.iop.org/1741-2552/11/i=3/a=036003?key=crossref.cd506521645b5c4b1839edf36a56ac4f},
volume = {11},
year = {2014}
}
@article{Zander2016,
abstract = {The effectiveness of today's human–machine interaction is limited by a communication bottleneck as operators are required to translate high-level concepts into a machine-mandated sequence of instructions. In contrast, we demonstrate effective, goal-oriented control of a computer system without any form of explicit communication from the human operator. Instead, the system generated the necessary input itself, based on real-time analysis of brain activity. Specific brain responseswere evoked by violating the operators' expectations to varying degrees. The evoked brain activity demonstrated detectable differences reflecting congruency with or deviations from the operators' expectations. Real-time analysis of this activity was used to build a user model of those expectations, thus representing the optimal (expected) state as perceived by the operator. Based on this model, which was continuously updated, the computer automatically adapted itself to the expectations of its operator. Further analyses showed this evoked activity to originate from themedial prefrontal cortex and to exhibit a linear correspondence to the degree of expectation violation. These findings extend our un- derstanding of human predictive coding and provide evidence that the information used to generate the user model is task-specific and reflects goal congruency. This paper demonstrates a form of interaction without any explicit input by the operator, enabling computer systems to become neuroadaptive, that is, to automatically adapt to specific aspects of their operator'smindset.Neuroadaptive technology significantlywidens the communication bottleneck and has the potential to fundamentally change the way we interact with technology.},
author = {Zander, Thorsten O. and Krol, Laurens R. and Birbaumer, Niels P. and Gramann, Klaus},
doi = {10.1073/pnas.1605155114},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Zander et al. - 2016 - Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity.pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences},
keywords = {electroencephalogram,human–computer interaction,neuroadaptive technology,passive brain–computer interfaces,predictive coding},
mendeley-groups = {Thesis,Thesis2},
month = {dec},
number = {52},
pages = {14898--14903},
pmid = {27956633},
publisher = {National Academy of Sciences},
title = {{Neuroadaptive technology enables implicit cursor control based on medial prefrontal cortex activity}},
url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1605155114},
volume = {113},
year = {2016}
}
@article{Nourmohammadi2018,
author = {Nourmohammadi, Amin and Jafari, Mohammad},
doi = {10.1109/THMS.2018.2830647},
isbn = {21682291 (ISSN)},
issn = {2168-2291},
journal = {IEEE Transactions on Human-Machine Systems},
mendeley-groups = {Thesis,Thesis2},
number = {May},
pages = {1--12},
title = {{A Survey on Unmanned Aerial Vehicle Remote Control Using Brain– Computer Interface}},
url = {https://ieeexplore.ieee.org/document/8362734/},
year = {2018}
}
@article{Zander2017,
abstract = {{\textless}p{\textgreater}Brain-computer interfaces can provide an input channel from humans to computers that depends only on brain activity, bypassing traditional means of communication and interaction. This input channel can be used to send explicit commands, but also to provide implicit input to the computer. As such, the computer can obtain information about its user that not only bypasses, but also goes beyond what can be communicated using traditional means. In this form, implicit input can potentially provide significant improvements to human-computer interaction. This paper describes a selection of work done by Team PhyPA (Physiological Parameters for Adaptation) at the Technische Universit{\"{a}}t Berlin to use brain-computer interfacing to enrich human-computer interaction.{\textless}/p{\textgreater}},
author = {Zander, Thorsten O. and Krol, Laurens R.},
doi = {10.3311/PPee.10435},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Zander, Krol - 2017 - Team PhyPA Brain-Computer Interfacing for Everyday Human-Computer Interaction.pdf:pdf},
issn = {2064-5279},
journal = {Periodica Polytechnica Electrical Engineering and Computer Science},
keywords = {brain,computer interaction,computer interface,human,neuroadaptive technology},
mendeley-groups = {Thesis,Thesis2},
month = {may},
number = {2},
pages = {209},
title = {{Team PhyPA: Brain-Computer Interfacing for Everyday Human-Computer Interaction}},
url = {https://pp.bme.hu/eecs/article/view/10435},
volume = {61},
year = {2017}
}
@incollection{Zander2010,
author = {Zander, Thorsten O. and Kothe, Christian and Jatzev, Sabine and Gaertner, Matti},
doi = {10.1007/978-1-84996-272-8_11},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Zander et al. - 2010 - Enhancing Human-Computer Interaction with Input from Active and Passive Brain-Computer Interfaces.pdf:pdf},
mendeley-groups = {Thesis,Thesis2},
pages = {181--199},
bookTitle={Brain-Computer Interfaces: Applying our Minds to Human-Computer Interaction},
publisher = {Springer, London},
title = {{Enhancing Human-Computer Interaction with Input from Active and Passive Brain-Computer Interfaces}},
url = {http://link.springer.com/10.1007/978-1-84996-272-8{\_}11},
year = {2010}
}
@book{Scholkopf2001,
  title={Learning with kernels: support vector machines, regularization, optimization, and beyond},
  author={Scholkopf, Bernhard and Smola, Alexander J},
  year={2001},
  publisher={MIT press}
}

@manual{GartnerHype2016,
title  = "Gartner Hype 2015",
mendeley-groups = {Thesis,Thesis2},
author = "Gartner, Inc.",
note   = "\url{https://www.gartner.com/newsroom/id/3114217%0A}",
year   = "2015 (accessed September 3, 2018)"
}

@manual{Labstreaminglayer,
title  = "Lab streaming layer (LSL)",
mendeley-groups = {Thesis,Thesis2},
author = "Swartz Center for Computational Neuroscience, UCSD",
note   = "\url{https://github.com/sccn/labstreaminglayer}",
year   = "2018 (accessed October 10, 2018)"
}


@manual{WorldCup2014,
title  = "Opening ceremony of FIFA World Cup: Paralyzed person kicks the first ball",
mendeley-groups = {Thesis,Thesis2},
author = "Technical University of Munich",
note   = "\url{https://www.tum.de/en/about-tum/news/press-releases/detail/article/31565/}",
year   = "2018 (accessed October 5, 2018)"
}

@article{Mallat1993,
abstract = {The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. They derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992)},
author = {Mallat, Stephane G. and Zhang, Zhifeng},
doi = {10.1109/78.258082},
isbn = {1053-587X},
issn = {19410476},
journal = {IEEE Transactions on Signal Processing},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {12},
pages = {3397--3415},
pmid = {22094447},
title = {{Matching Pursuits With Time-Frequency Dictionaries}},
url = {http://ieeexplore.ieee.org/document/258082/},
volume = {41},
year = {1993}
}
@article{Moher2009,
abstract = {Systematic reviews provide clinical practice recommendations that are based on evaluation of primary evidence. When systematic reviews with the same aims have different conclusions, it is difficult to ascertain which review reported the most credible and robust findings. This study examined five systematic reviews that have investigated the effectiveness of Pilates exercise in people with chronic low back pain. A four-stage process was used to interpret findings of the reviews. This process included comparison of research questions, included primary studies, and the level and quality of evidence of systematic reviews. Two independent reviewers assessed the level of evidence and the methodological quality of systematic reviews, using the National Health and Medical Research Council hierarchy of evidence, and the Revised Assessment of Multiple Systematic Reviews respectively. Any disagreements were resolved by a third researcher. A high level of consensus was achieved between the reviewers. Conflicting findings were reported by the five systematic reviews regarding the effectiveness of Pilates in reducing pain and disability in people with chronic low back pain. Authors of the systematic reviews included primary studies that did not match their questions in relation to treatment or population characteristics. A total of ten primary studies were identified across five systematic reviews. Only two of the primary studies were included in all of the reviews due to different inclusion criteria relating to publication date and status, definition of Pilates, and methodological quality. The level of evidence of reviews was low due to the methodological design of the primary studies. The methodological quality of reviews varied. Those which conducted a meta-analysis obtained higher scores. There is inconclusive evidence that Pilates is effective in reducing pain and disability in people with chronic low back pain. This is due to the small number and poor methodological quality of primary studies. The Revised Assessment of Multiple Systematic Reviews provides a useful method of appraising the methodological quality of systematic reviews. Individual item scores, however, should be examined in addition to total scores, so that significant methodological flaws of systematic reviews are not missed, and results are interpreted appropriately. (348 words)},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Moher, David and Liberati, Alessandro and Tetzlaff, Jennifer and Altman, Douglas G. and Altman, Doug and Antes, Gerd and Atkins, David and Barbour, Virginia and Barrowman, Nick and Berlin, Jesse A. and Clark, Jocalyn and Clarke, Mike and Cook, Deborah and D'Amico, Roberto and Deeks, Jonathan J. and Devereaux, P. J. and Dickersin, Kay and Egger, Matthias and Ernst, Edzard and G{\o}tzsche, Peter C. and Grimshaw, Jeremy and Guyatt, Gordon and Higgins, Julian and Ioannidis, John P.A. and Kleijnen, Jos and Lang, Tom and Magrini, Nicola and McNamee, David and Moja, Lorenzo and Mulrow, Cynthia and Napoli, Maryann and Oxman, Andy and Pham, B{\'{a}} and Rennie, Drummond and Sampson, Margaret and Schulz, Kenneth F. and Shekelle, Paul G. and Tovey, David and Tugwell, Peter},
doi = {10.1371/journal.pmed.1000097},
eprint = {arXiv:1011.1669v3},
isbn = {0031-9023},
issn = {15491277},
journal = {PLoS Medicine},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {jul},
number = {7},
pages = {e1000097},
pmid = {19621072},
title = {{Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19621072 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2707599 http://dx.plos.org/10.1371/journal.pmed.1000097},
volume = {6},
year = {2009}
}
@article{QuianQuiroga2003,
abstract = {The application of a recently proposed denoising implementation for obtaining event-related potentials (ERPs) at the single-trial level is shown. We study its performance in simulated data as well as in visual and auditory ERPs. For the simulated data, the method gives a significantly better reconstruction of the single-trial event-related responses in comparison with the original data and also in comparison with a reconstruction based on conventional Wiener filtering. Moreover, with wavelet denoising we obtain a significantly better estimation of the amplitudes and latencies of the simulated ERPs. For the real data, the method clearly improves the visualization of both visual and auditory single-trial ERPs. This allows the calculation of better averages as well as the study of systematic or unsystematic variations between trials. Since the method is fast and parameter free, it could complement the conventional analysis of ERPs. {\textcopyright} 2002 Elsevier Science Ireland Ltd. All rights reserved.},
author = {{Quian Quiroga}, R. and Garcia, H.},
doi = {10.1016/S1388-2457(02)00365-6},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Quian Quiroga, Garcia - 2003 - Single-trial event-related potentials with wavelet denoising.pdf:pdf},
isbn = {05315131 (ISSN)},
issn = {13882457},
journal = {Clinical Neurophysiology},
keywords = {Denoising,Electroencephalogram,Evoked potential,Single-trial,Wavelet,Wiener},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {feb},
number = {2},
pages = {376--390},
pmid = {12559247},
publisher = {Elsevier},
title = {{Single-trial event-related potentials with wavelet denoising}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12559247},
volume = {114},
year = {2003}
}
@article{Shah2015,
abstract = {Amplitude-integrated electroencephalography (aEEG) is a method for continuous monitoring of brain activity that is increasingly used in the neonatal intensive care unit. In its simplest form, aEEG is a processed single-channel electroencephalogram that is filtered and time-compressed. Current evidence demonstrates that aEEG is useful to monitor cerebral background activity, diagnose and treat seizures and predict neurodevelopmental outcomes for preterm and term infants. This review aims to explain the fundamentals behind aEEG and its clinical applications.},
author = {Shah, Nidhi Agrawal and Wusthoff, Courtney Jane},
doi = {10.1136/archdischild-2013-305676},
isbn = {1743-0585},
issn = {17430593},
journal = {Archives of Disease in Childhood: Education and Practice Edition},
keywords = {Evidence Based Medicine,Neonatology,Neurology},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {apr},
number = {2},
pages = {75--81},
pmid = {25035312},
title = {{How to use: Amplitude-integrated EEG (aEEG)}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25035312 http://ep.bmj.com/lookup/doi/10.1136/archdischild-2013-305676},
volume = {100},
year = {2015}
}
@article{Domingo2012,
abstract = {Currently, over a billion people including children (or about 15{\%} of the worlds population) are estimated to be living with disability. The lack of support services can make handicapped people overly dependent on their families, which prevents them from being economically active and socially included. The Internet of Things can offer people with disabilities the assistance and support they need to achieve a good quality of life and allows them to participate in the social and economic life. In this paper, an overview of the Internet of Things for people with disabilities is provided. For this purpose, the proposed architecture of the Internet of Things is introduced. Different application scenarios are considered in order to illustrate the interaction of the components of the Internet of Things. Critical challenges have been identified and addressed. {\textcopyright} 2011 Elsevier Ltd. All rights reserved.},
author = {Domingo, Mari Carmen},
doi = {10.1016/j.jnca.2011.10.015},
isbn = {1084-8045},
issn = {10848045},
journal = {Journal of Network and Computer Applications},
keywords = {Applications,Architecture,Internet of Things,People with disabilities,Research challenges},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {mar},
number = {2},
pages = {584--596},
publisher = {Academic Press},
title = {{An overview of the Internet of Things for people with disabilities}},
url = {https://www.sciencedirect.com/science/article/pii/S1084804511002025},
volume = {35},
year = {2012}
}
@article{Jackson2014,
author = {Jackson, Alice F. and Bolger, Donald J.},
doi = {10.1111/psyp.12283},
issn = {00485772},
journal = {Psychophysiology},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {nov},
number = {11},
pages = {1061--1071},
title = {{The neurophysiological bases of EEG and EEG measurement: A review for the rest of us}},
url = {http://doi.wiley.com/10.1111/psyp.12283},
volume = {51},
year = {2014}
}
@article{Haberman2012,
abstract = {Single ended (SE) amplifiers allow implementing biopotential front-ends with a reduced number of parts, being well suited for preamplified electrodes or compact EEG headboxes. On the other hand, given that each channel has independent gain; mismatching between these gains results in poor common-mode rejection ratios (CMRRs) (about 30 dB considering 1{\%} tolerance components). This work proposes a scheme for multichannel EEG acquisition systems based on SE amplifiers and a novel digital driven right leg (DDRL) circuit, which overcome the poor CMRR of the front-end stage providing a high common mode reduction at power line frequency (up to 80 dB). A functional prototype was built and tested showing the feasibility of the proposed technique. It provided EEG records with negligible power line interference, even in very aggressive EMI environments.},
author = {Haberman, Marcelo Alejandro and Spinelli, Enrique Mario},
doi = {10.1109/TBCAS.2012.2190733},
issn = {19324545},
journal = {IEEE Transactions on Biomedical Circuits and Systems},
keywords = {Biopotential amplifier,Biopotential front-end,Common-mode rejection ratio (CMRR),Driven right leg (DRL) circuit,Multichannel EEG,Power line interference},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {dec},
number = {6},
pages = {614--618},
title = {{A multichannel EEG acquisition scheme based on single ended amplifiers and digital DRL}},
url = {http://ieeexplore.ieee.org/document/6183494/},
volume = {6},
year = {2012}
}
@article{Yuste2017,
abstract = {Artificial intelligence and brain–computer interfaces must respect and preserve people's privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.},
author = {Yuste, Rafael and Goering, Sara and {Ag{\"{u}}eray Arcas}, Blaise and Bi, Guoqiang and Carmena, Jose M. and Carter, Adrian and Fins, Joseph J. and Friesen, Phoebe and Gallant, Jack and Huggins, Jane E. and Illes, Judy and Kellmeyer, Philipp and Klein, Eran and Marblestone, Adam and Mitchell, Christine and Parens, Erik and Pham, Michelle and Rubel, Alan and Sadato, Norihiro and Sullivan, Laura Specker and Teicher, Mina and Wasserman, David and Wexler, Anna and Whittaker, Meredith and Wolpaw, Jonathan},
doi = {10.1038/551159a},
issn = {14764687},
journal = {Nature},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {nov},
number = {7679},
pages = {159--163},
pmid = {29120438},
title = {{Four ethical priorities for neurotechnologies and AI}},
url = {http://www.nature.com/doifinder/10.1038/551159a},
volume = {551},
year = {2017}
}
@article{Wei2017,
abstract = {Background Epilepsy is a common neurological disease, and electroencephalogram (EEG) contains massive epilepsy information. Automatic recognition of epileptic discharges has great significance in diagnosis of epilepsy. New method This paper proposes a novel automatic recognition of epileptic waves method in EEG signals based on shape similarity in time-series sequence directly. Merger of the increasing and decreasing sequences (MIDS) was used to improve the recognition accuracy and reduce the computation cost. Then shape templates were designed, and the modified Hausdorff distance was employed to measure the shape similarity of waveforms in template matching part. This approach imitates human visual cognitive process to analyze EEG and employs image recognition method into one-dimensional signals, which is a direct, original and effective method. Results 373 epileptic discharge fragments marked by clinicians from 20 patients' EEG recordings were selected. By fusing significance rules, 98.39{\%} of them were recognized, with the false recognition rate 1.1{\%}. Comparison with existing methods Experimental results indicate that the proposed approach yielded better performance for interictal epileptiform discharges (IEDS) recognition compared with the previous methods. Conclusions The proposed approach has good performance and high stability in automatic recognition of epileptic discharges both in ictal and interictal period, which could support the diagnosis of epilepsy greatly.},
author = {Wei, Zuo Chen and Zou, Jun Zhong and Zhang, Jian and Chen, Lan Lan},
doi = {10.1016/j.bspc.2016.12.007},
issn = {17468108},
journal = {Biomedical Signal Processing and Control},
keywords = {Automatic recognition,Epileptic EEG,Modified Hausdorff distance,Shape similarity,Template matching},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pages = {236--244},
publisher = {Elsevier Ltd},
title = {{Automatic recognition of epileptic discharges based on shape similarity in time-domain}},
url = {http://dx.doi.org/10.1016/j.bspc.2016.12.007},
volume = {33},
year = {2017}
}
@article{Zhang2013,
abstract = {This paper proposes a new automatic detection method of Interictal Epileptiform Discharges (IED) based on the merger of the increasing and decreasing sequences (MIDS) to improve IED detection rate. Firstly, increasing and decreasing sequences as well as complete and incomplete waves are reviewed to highlight the characteristics of clinical visual detection of IED. The sequence merging rules and algorithms are consequently proposed for time-domain electroencephalogram (EEG) signals. Experimental results demonstrate that the performance MIDS detection on rhythm waves and slow waves are very close to clinical visual detection. Secondly, the MIDS detection method is applied to IED fragments according to IED features in the time-domain. The results show that most IED fragments are recognized, although with some false detection of non-IED fragments. To reduce such false detection rate, Support Vector Machine (SVM) was applied with 17 characteristics and a training over 232 fragments from 3 patients' EEG recordings. With the SVM improvement, out-of-sample clinical EEG recordings of 32 suspected epilepsy patients were analyzed and 95.9{\%} of the IED fragments marked by clinicians were successfully detected. The results show that the proposed algorithm performs well in IED detection and is a promising candidate in assisting clinicians' epilepsy diagnosis. {\textcopyright} 2013 Elsevier B.V.},
author = {Zhang, Jian and Zou, Junzhong and Wang, Min and Chen, Lanlan and Wang, Chunmei and Wang, Guisong},
doi = {10.1016/j.neucom.2012.11.017},
isbn = {978-3-03785-545-4},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Automatic detection,Epileptic EEG,Merger of increasing and decreasing sequences (MID,Support vector machine},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {35--43},
publisher = {Elsevier},
title = {{Automatic detection of interictal epileptiform discharges based on time-series sequence merging method}},
url = {http://dx.doi.org/10.1016/j.neucom.2012.11.017},
volume = {110},
year = {2013}
}
@article{Jansen1991,
abstract = {The history of quantitative, computerized electroencephalogram (EEG) analysis is reviewed. It is shown that, until very recently, the basic approach to EEG analysis involved the assumption that the EEG is stochastic. Consequently, statistical pattern recognition techniques, segmentation procedures, syntactic methods, knowledge-based approaches, and even artificial neural network methods have been developed with different levels of success. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of non-linear dynamics, and especially the theory of chaos, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing non-linearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. Evidence supporting the non-linear dynamics paradigm is reviewed, and possible research paths are indicated. {\textcopyright} 1991.},
author = {Jansen, Ben H.},
doi = {10.1016/0020-7101(91)90090-2},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Quantitive Analysis of EEG.1991.pdf:pdf},
isbn = {0020-7101 (Print)$\backslash$r0020-7101 (Linking)},
issn = {00207101},
journal = {International Journal of Bio-Medical Computing},
keywords = {Adaptive segmentation,Chaos theory,Non-linear modeling,Pattern recognition,Quantitative EEG analysis},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {95--123},
pmid = {2032756},
title = {{Quantitative analysis of electroencephalograms: is there chaos in the future?}},
volume = {27},
year = {1991}
}
@article{MullerPutz2015,
abstract = {The concept of neuro-information systems (neuroIS) has emerged in the IS discipline recently. Since the neuroIS field?s genesis, several neuroIS papers have been published. Investigating empirical papers published in scientific journals and conference proceedings reveals that electroencephalography (EEG) is a widely used tool. Thus, considering its relevance in contemporary research and the fact that it will also play a major role in future neuroIS research, we describe EEG from a layman?s perspective. Because previous EEG descriptions in the neuroIS literature have only scantily outlined theoretical and methodological aspects related to this tool, we urgently need a more thorough one. As such, we inform IS scholars about the fundamentals of EEG in a compact way and discuss EEG?s potential for IS research. Based on the knowledge base provided in this paper, IS researchers can make an informed decision about whether EEG could, or should, become part of their toolbox.},
author = {M{\"{u}}ller-Putz, Gernot R. and Riedl, Ren{\'{e}} and Wriessnegger, Selina C.},
issn = {15293181},
journal = {Communications of the Association for Information Systems},
keywords = {Brain,Construct,EEG guidelines,Electroencephalography (EEG),Error-related negativity (ERN),Event-related potential (ERP),Frequency bands,Measurement,Methodology,N200,N400,NeuroIS,Neuron,P200,P300,Research method,Spontaneous EEG},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
number = {1},
pages = {911--948},
title = {{Electroencephalography (EEG) as a research tool in the information systems discipline: Foundations, measurement, and applications}},
volume = {37},
year = {2015}
}
@article{Jaiswal2017,
abstract = {Background and objective According to the World Health Organization (WHO) epilepsy affects approximately 45–50 million people. Electroencephalogram (EEG) records the neurological activity in the brain and it is used to identify epilepsy. Visual inspection of EEG signals is a time-consuming process and it may lead to human error. Feature extraction and classification are two main steps that are required to build an automated epilepsy detection framework. Feature extraction reduces the dimensions of the input signal by retaining informative features and the classifier assigns a proper class label to the extracted feature vector. Our aim is to present effective feature extraction techniques for automated epileptic EEG signal classification. Methods In this study, two effective feature extraction techniques (Local Neighbor Descriptive Pattern [LNDP] and One-dimensional Local Gradient Pattern [1D-LGP]) have been introduced to classify epileptic EEG signals. The classification between epileptic seizure and non-seizure signals is performed using different machine learning classifiers. The benchmark epilepsy EEG dataset provided by the University of Bonn is used in this research. The classification performance is evaluated using 10-fold cross validation. The classifiers used are the Nearest Neighbor (NN), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN). The experiments have been repeated for 50 times. Results LNDP and 1D-LGP feature extraction techniques with ANN classifier achieved the average classification accuracy of 99.82{\%} and 99.80{\%}, respectively, for the classification between normal and epileptic EEG signals. Eight different experimental cases were tested. The classification results were better than those of some existing methods. Conclusions This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals.},
author = {Jaiswal, Abeg Kumar and Banka, Haider},
doi = {10.1016/j.bspc.2017.01.005},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Local Pattern Transformation methods for feature extraction in EEG.pdf:pdf},
issn = {17468108},
journal = {Biomedical Signal Processing and Control},
keywords = {Classification,Electroencephalogram (EEG) signals,Feature extraction,Local Neighbor Descriptive Pattern (LNDP),One-dimensional Local Gradient Pattern (1D-LGP)},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pages = {81--92},
publisher = {Elsevier Ltd},
title = {{Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals}},
url = {http://dx.doi.org/10.1016/j.bspc.2017.01.005},
volume = {34},
year = {2017}
}
@article{Uchida1999,
abstract = {Zero-cross and zero-derivative period amplitude analysis (PAA) data were compared with power spectral analysis (PSA) data obtained with the fast Fourier transform in all-night sleep EEG from 10 subjects. Although PAA zero-cross-integrated amplitude showed good agreement with PSA power in 0.3-2 Hz, zero-cross analysis appears relatively ineffective in measuring 2-4 Hz and above waves. However, PAA zero-derivative measures of peak-trough amplitude correlated well with PSA power in 2-4 Hz. Thus, while PAA appears able to measure the entire EEG spectrum, the analytic technique should be changed from zero cross to zero derivative at about 2 Hz in human sleep EEG. PAA and PSA both demonstrate robust and interrelated across-night oscillations in three frequency bands: delta (0.3-4 Hz); sigma (12-16 Hz); and fast beta (20-40 Hz). The frequencies between delta and sigma, and between sigma and fast beta, did not show clear across-night oscillations using either method, and the two methods showed lower epoch-to-epoch agreement in these intermediate bands. The causes of this reduced agreement are not immediately clear, nor is it obvious which method gives more valid results. We believe that the three strongly oscillating frequency bands represent fundamental properties of the human sleep EEG that provide important clues to underlying physiological mechanisms. These mechanisms are more likely to be understood if their dynamic properties are preserved and measured naturalistically rather than being forced into arbitrary sleep stages or procrustean models. Both PAA and PSA can be employed for such naturalistic studies. PSA has the advantages of applying the same analytic method across the EEG spectrum and rests on more fully developed theory. Combined zero-cross and zero-derivative PAA demonstrates EEG oscillations that closely parallel those observed with spectral power, and the PAA measures do not rely on assumptions about the spectral composition of the signal. In addition, both PAA techniques can measure the relative contributions of wave amplitude and incidence to total power. These waveform characteristics represent different biological processes and respond differentially to a wide range of experimental conditions. Copyright (C) 1999 Elsevier Science Inc.},
author = {Uchida, Sunao and Feinberg, Irwin and March, Jonathan D. and Atsumi, Yoshikata and Maloney, Tom},
doi = {10.1016/S0031-9384(99)00049-9},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/PAA vs FFT for Sleep Reseatch Introduction.pdf:pdf},
isbn = {0031-9384 (Print) 0031-9384 (Linking)},
issn = {00319384},
journal = {Physiology and Behavior},
keywords = {Delta,FFT power spectral analysis,Fast beta,Period amplitude analysis,Sigma spindle,Sleep EEG},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {1},
pages = {121--131},
pmid = {10463638},
title = {{A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG}},
volume = {67},
year = {1999}
}
@article{Unakafova2013,
abstract = {Permutation entropy, introduced by Bandt and Pompe, is a conceptually simple and well-interpretable measure of time series complexity. In this paper, we propose efficient methods for computing it and related ordinal-patterns-based characteristics. The methods are based on precomputing values of successive ordinal patterns of order d, considering the fact that they are “overlapped” in d points, and on precomputing successive values of the permutation entropy related to “overlapping” successive time-windows. The proposed methods allow for measurement of the complexity of very large datasets in real-time},
author = {Unakafova, Valentina and Keller, Karsten},
doi = {10.3390/e15104392},
issn = {1099-4300},
journal = {Entropy},
keywords = {complexity,efficient computing,ordinal patterns,permutation entropy},
mendeley-groups = {EEGWaveformAnalysis,Histogram of Oriented Gradients P300,Thesis2},
number = {10},
pages = {4392--4415},
title = {{Efficiently Measuring Complexity on the Basis of Real-World Data}},
url = {http://www.mdpi.com/1099-4300/15/10/4392/},
volume = {15},
year = {2013}
}
@article{Eberhart1989,
abstract = {CaseNet, a neural-network-based tool for the analysis and classification of EEG waveforms, is described. The development of CaseNet, and of the EEG signal preprocessing procedures required to use CaseNet for multichannel epileptiform spike detection are reviewed. Results using CaseNet to detect epileptiform spikes in a four-channel offline system are presented. The spike detection work described is part of a larger cooperative program. Program goals include online spike detection and online seizure prediction and detection},
author = {Eberhart, R.C. and Dobbins, R.W. and Webber, W.R.S.},
doi = {10.1109/IEMBS.1989.96587},
journal = {Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
pages = {2046--2047},
title = {{EEG waveform analysis using CaseNet}},
url = {http://ieeexplore.ieee.org/document/96587/},
year = {1989}
}
@article{Lutz2008,
abstract = {The rate at which the world's population ages is important to policymakers, especially those dealing with pensions and healthcare. In particular, it is vital to anticipate periods of rapid change, when adjustments will be the most difficult. A new estimate of ageing trends in the global population combines traditional measures, based on a fixed age boundary, with new concepts of age. New concepts include a fixed remaining life expectancy, as today's 60 year old is 'younger' than a 60 year old from 1900 and has more years left to live. No matter how you look at it, the estimates are that the world's population is ageing with increasing speed. Global ageing is likely to peak between 2020 and 2030, and then decelerate, although there will be further increases in the level of ageing throughout the century. The timing of 'peak ageing' is based on past patterns of fertility. In the United States and in parts of Western Europe the 'baby boomers' are a big factor. In China, the timing of fertility control policies has a big influence.},
author = {Lutz, Wolfgang and Sanderson, Warren and Scherbov, Sergei},
doi = {10.1038/nature06516},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Lutz, Sanderson, Scherbov - 2008 - The coming acceleration of global population ageing(2).pdf:pdf},
issn = {0028-0836},
journal = {Nature},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {feb},
number = {7179},
pages = {716--719},
publisher = {Nature Publishing Group},
title = {{The coming acceleration of global population ageing}},
url = {http://www.nature.com/articles/nature06516},
volume = {451},
year = {2008}
}
@article{Giagante2003,
abstract = {Objective: The purpose of this study is to identify specific clinical-electroencephalogram (EEG) patterns at seizure onset in patients with hippocampal sclerosis (HS). Methods: Sixty-six ictal video-EEG recordings corresponding to 26 patients with HS have been reviewed, focusing on the EEG features found during the first 30 ictal s. The EEG activity has been classified into the following groups: (A) according to spatial distribution: type 1: temporal electrodes on one side; type 2: temporal and adjacent frontal electrodes on one side; and type 3: non-lateralizing electrographic activity; and (B) according to morphology; subtype (a): regular 5-9 Hz rhythmic activity (RA); subtype (b): low-voltage rapid activity, followed by a 5-9 Hz RA; and subtype (c): irregular EEG sharp waves. We analyzed the clinical symptoms sequence and established the relationship with the ictal EEG patterns. Results: Considering spatial distribution and morphology, the most frequent ictal EEG patterns were type 1 (57{\%}), type 2 (37{\%}), and subtype (a): 62{\%}; subtype (b): 27{\%}; and subtype (c): 11{\%}. The sequence of clinical symptoms observed was: aura→behavioral arrest→oro-alimentary automatisms→unilateral hand automatisms. All seizures with aura and including two or more symptoms of the clinical sequence (65{\%}) were associated with a 1a, 1b, 2a or 2b EEG pattern. Conclusions: The identification of a specific clinical-EEG pattern provides a useful tool for the epileptogenic zone localization in non-invasive pre-surgical assessment of patients with hippocampal sclerosis. Significance: The identification of a specific clinical-EEG pattern associated to neuroimaging findings and neuropsychological testing allows indicating surgery for the treatment of epilepsy in patients with hippocampal sclerosis, without performing any further complementary studies. {\textcopyright} 2003 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.},
author = {Giagante, B. and Oddo, S. and Silva, W. and Consalvo, D. and Centurion, E. and D'Alessio, L. and Solis, P. and Salgado, P. and Seoane, E. and Saidon, P. and Kochen, S.},
doi = {10.1016/S1388-2457(03)00284-0},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Clinical-EEG patterns.pdf:pdf},
isbn = {1388-2457},
issn = {13882457},
journal = {Clinical Neurophysiology},
keywords = {Clinical patterns,Electrical patterns,Hippocampal sclerosis,Temporal lobe epilepsy},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {12},
pages = {2286--2293},
pmid = {14652088},
title = {{Clinical-electroencephalogram patterns at seizure onset in patients with hippocampal sclerosis}},
volume = {114},
year = {2003}
}
@inproceedings{Nicolaou2010,
abstract = {This paper investigates the use of Permutation Entropy (PE) as a feature for mental task classification for a Brain-Computer Interface system. PE is a recently introduced measure which quantifies signal complexity by measuring the departure of a time series from a random one. More regular signals are characterized by lower PE values. Here, PE is utilized to characterize signals from electroencephalograms of 3 subjects performing 4 motor imagery tasks, which are then classified using a Support Vector Machine. Even though it is possible to obtain 100{\%} single-trial classification accuracy, this is very much subject-dependent. {\textcopyright}2010 IEEE.},
author = {Nicolaou, Nicoletta and Georgiou, Julius},
booktitle = {2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010},
doi = {10.1109/BIOCAS.2010.5709568},
isbn = {9781424472703},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {nov},
pages = {49--52},
publisher = {IEEE},
title = {{Permutation entropy: A new feature for brain-computer interfaces}},
url = {http://ieeexplore.ieee.org/document/5709568/},
year = {2010}
}
@article{Goss-Varley2017,
abstract = {Intracortical microelectrodes have shown great success in enabling locked-in patients to interact with computers, robotic limbs, and their own electrically driven limbs. The recent advances have inspired world-wide enthusiasm resulting in billions of dollars invested in federal and industrial sponsorships to understanding the brain for rehabilitative applications. Additionally, private philanthropists have also demonstrated excitement in the field by investing in the use of brain interfacing technologies as a means to human augmentation. While the promise of incredible technologies is real, caution must be taken as implications regarding optimal performance and unforeseen side effects following device implantation into the brain are not fully characterized. The current study is aimed to quantify any motor deficit caused by microelectrode implantation in the motor cortex of healthy rats compared to non-implanted controls. Following electrode insertion, rats were tested on an open-field grid test to study gross motor function and a ladder test to study fine motor function. It was discovered that rats with chronically indwelling intracortical microelectrodes exhibited up to an incredible 527{\%} increase in time to complete the fine motor task. This initial study defines the need for further and more robust behavioral testing of potential unintentional harm caused by microelectrode implantation.},
author = {Goss-Varley, Monika and Dona, Keith R. and McMahon, Justin A. and Shoffstall, Andrew J. and Ereifej, Evon S. and Lindner, Sydney C. and Capadona, Jeffrey R.},
doi = {10.1038/s41598-017-15623-y},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Goss-Varley et al. - 2017 - Microelectrode implantation in motor cortex causes fine motor deficit Implications on potential consideratio.pdf:pdf},
isbn = {4159801715623},
issn = {20452322},
journal = {Scientific Reports},
keywords = {Humanities and Social Sciences,Science,multidisciplinary},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {dec},
number = {1},
pages = {15254},
publisher = {Nature Publishing Group},
title = {{Microelectrode implantation in motor cortex causes fine motor deficit: Implications on potential considerations to Brain Computer Interfacing and Human Augmentation}},
url = {http://www.nature.com/articles/s41598-017-15623-y},
volume = {7},
year = {2017}
}
@article{Subha2010,
abstract = {The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.},
author = {Subha, D. Puthankattil and Joseph, Paul K. and {Acharya U}, Rajendra and Lim, Choo Min},
doi = {10.1007/s10916-008-9231-z},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Subha et al. - 2010 - EEG signal analysis a survey.pdf:pdf},
isbn = {0990212106},
issn = {01485598},
journal = {Journal of medical systems},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {apr},
number = {2},
pages = {195--212},
pmid = {20433058},
publisher = {Springer US},
title = {{EEG signal analysis: a survey.}},
url = {http://link.springer.com/10.1007/s10916-008-9231-z},
volume = {34},
year = {2010}
}
@inproceedings{DuPreez2007,
abstract = {Certification of medical equipment requires thorough testing. Prior to final testing of equipment (such as monitoring equipment) on a human volunteer, it is necessary to test equipment via emulation. This paper presents a bio-simulator that generates electrocardiogram (ECG) and electromyogram (EMG) signals by breaking a signal up into pulses and integrating these pulses, and electroencephalogram (EEG) signals by waveform shaping. The other set of signals generated are sinusoidal, square and triangular signals used in the calibration of medical equipment. As low frequency signals are needed for bio-medical applications, the paper also details how this was achieved (using capacitor multipliers). A complementary metal oxide semiconductor (CMOS) 0.35 mum process (Austria Microsystems (XMS)) is used for the implementation},
author = {{Du Preez}, C. C. and Sinha, S. and {Du Plessis}, M.},
booktitle = {Proceedings of the International Semiconductor Conference, CAS},
doi = {10.1109/SMICND.2006.283925},
isbn = {1424401097},
keywords = {Electrocardiogram,Electroencephalogram,Electromyogram,Programmable frequency divider,Sinusoidal,Square,Triangular,Waveform shaper},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {sep},
pages = {29--38},
publisher = {IEEE},
title = {{CMOS ECG, EEG and EMG waveform bio-simulator}},
url = {http://ieeexplore.ieee.org/document/4063152/},
volume = {1},
year = {2007}
}
@inproceedings{Park1992,
author = {Park, Seung-Hun and Chang, Tae G.},
booktitle = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
doi = {10.1109/IEMBS.1992.5761589},
isbn = {0-7803-0785-2},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {oct},
pages = {2564--2565},
publisher = {IEEE},
title = {{EEG waveform detection based on the search of distinctive line-segments}},
url = {http://ieeexplore.ieee.org/document/5761589/},
year = {1992}
}
@article{Malinowska2007,
abstract = {Adaptive time-frequency approximations, implemented via the matching pursuit algorithm, offer description of local signals structures in terms of their time occurrence and width, frequency and amplitude. This allows to construct explicit filters for finding EEG waveforms, known from the visual analysis, in the matching pursuit decomposition of signals. In such a way detectors of relevant structures of both transient and oscillatory nature can be constructed in the space of physically meaningful parameters. This study presents evaluation of changes of power and frequency of sleep spindles and delta waves, related to the depth of the sleep, which were previously assessed in a qualitative way. We confirm quantitatively the decrease of frequencies of sleep spindles and delta waves with the depth of the sleep. {\textcopyright} 2006 Elsevier Ltd. All rights reserved.},
author = {Malinowska, Urszula and Durka, Piotr J. and Zygierewicz, Jaros{\l}aw and Szelenberger, Waldemar and Wakarow, Andrzej},
doi = {10.1016/j.compbiomed.2006.08.005},
isbn = {0010-4825 (Print)},
issn = {00104825},
journal = {Computers in Biology and Medicine},
keywords = {Delta waves,EEG,Hypnogram,Matching pursuit,Sleep spindles,Time-frequency,Wavelets},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {apr},
number = {4},
pages = {534--541},
pmid = {16996048},
title = {{Explicit parameterization of sleep EEG transients}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/16996048 http://linkinghub.elsevier.com/retrieve/pii/S0010482506001351},
volume = {37},
year = {2007}
}
@article{Uchida1996,
abstract = {This article critically reviews 8 computer implementations of Fujimori's method for EEG waveform recognition, with methodological considerations for the application of this method to the analysis of all-night sleep EEG. Fujimori's method has been considered one of the most appropriate waveform analyses for EEG. This kind of analysis is advantageous for measuring frequency and amplitude of each EEG wave separately. However, current implementations have drawbacks which must be resolved before they can be used on all-night sleep EEG. An optimal sampling rate should be determined which is appropriate to the purpose of analysis. Amplitude thresholds for wave recognition, which are now set arbitrarily, should also be improved. Measurement of waves in higher orders of superimposition is also necessary, although existing systems are limited to the second order. Additional algorithms, such as for the separate detection of sleep slow waves, may be useful. Further applications for Fujimori's method are suggested.},
author = {Uchida, Sunao and Matsuura, Masato and Ogata, Shigeki and Yamamoto, Takuji and Aikawa, Naoyuki},
doi = {10.1016/0165-0270(95)00115-8},
isbn = {0165-0270 (Print)},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {Computer analysis,EEG waveform recognition method,Fujimori's method,Methodology,Sleep,Time domain analysis},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {jan},
number = {1},
pages = {1--12},
pmid = {8869478},
title = {{Computerization of Fujimori's method of waveform recognition a review and methodological considerations for its application to all-night sleep EEG}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/8869478},
volume = {64},
year = {1996}
}
@article{Edelman1997,
author = {Edelman, S and Intrator, N and Poggio, T},
journal = {Unpublished manuscript},
mendeley-groups = {Histogram of Oriented Gradients P300,Thesis,Thesis2},
title = {{Complex cells and object recognition}},
pages = {1--12},
year = {1997}
}
@article{Mak2012,
abstract = {The purpose of this study was to identify electroencephalography (EEG) features that correlate with P300-based brain-computer interface (P300 BCI) performance in people with amyotrophic lateral sclerosis (ALS). Twenty people with ALS used a P300 BCI spelling application in copy-spelling mode. Three types of EEG features were found to be good predictors of P300 BCI performance: (1) the root-mean-square amplitude and (2) the negative peak amplitude of the event-related potential to target stimuli (target ERP) at Fz, Cz, P3, Pz, and P4; and (3) EEG theta frequency (4.5-8 Hz) power at Fz, Cz, P3, Pz, P4, PO7, PO8 and Oz. A statistical prediction model that used a subset of these features accounted for {\textgreater}60{\%} of the variance in copy-spelling performance (p {\textless} 0.001, mean R(2) = 0.6175). The correlations reflected between-subject, rather than within-subject, effects. The results enhance understanding of performance differences among P300 BCI users. The predictors found in this study might help in: (1) identifying suitable candidates for long-term P300 BCI operation; (2) assessing performance online. Further work on within-subject effects needs to be done to establish whether P300 BCI user performance could be improved by optimizing one or more of these EEG features.},
author = {Mak, Joseph N. and McFarland, Dennis J. and Vaughan, Theresa M. and McCane, Lynn M. and Tsui, Phillippa Z. and Zeitlin, Debra J. and Sellers, Eric W. and Wolpaw, Jonathan R.},
doi = {10.1088/1741-2560/9/2/026014},
file = {:Users/rramele/Desktop/Mak{\_}etal2012.pdf:pdf},
isbn = {1741-2552 (Electronic)$\backslash$r1741-2552 (Linking)},
issn = {17412560},
journal = {Journal of Neural Engineering},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,EEGWaveformAnalysis,Thesis2},
number = {2},
pmid = {22350501},
title = {{EEG correlates of P300-based brain-computer interface (BCI) performance in people with amyotrophic lateral sclerosis}},
volume = {9},
pages = {1--11},
year = {2012}
}
@article{Rodenbeck2006,
abstract = {Question of the study The reliable evaluation of polysomnographic recordings (PSG) is an essential precondition for good clinical practice in sleep medicine. Although the scoring rules of Rechtschaffen and Kales [86] are internationally well established, they leave some room for different interpretations, and this may contribute to the limited reliability of visual sleep scoring. The German Sleep Society (DGSM) has set up a task force to devise ways to improve scoring reliability in the framework of their quality management programme. The intention was not to revise the rules of Rechtschaffen and Kales (R{\&}K), but to facilitate their reliable application in sleep scoring and to support the development of standardized algorithms for computerized sleep analysis. Methods The task force was formed in September 2004 as a subcommittee of the educational panel of the DGSM. The members of the task force are experienced in sleep scoring and have a background either in physiology, neurology, psychiatry, psychology, or biology. The aim of the task force was to provide interpretation aids and, if needed, specifications or amendments to the R{\&}K rules for the scoring of sleep electroencephalogram (EEG) waveforms and patterns. Decisions were based on the nominal group technique of a nominal panel as the formal consensus-building process. The consensus process was based on scoring and face-to-face discussions of at least 40 examples for each pattern in four 2-day meetings. Results Relevant EEG patterns for sleep stage scoring are alpha, theta, and delta waves, sleep spindles, K-complexes, vertex sharp waves, and sawtooth waves. If definitions for a given EEG pattern differed in the literature, the nominal group technique resulted in specifications and amended scoring rules for these EEG patterns. A second part including a series of examples with explanatory comments for each of these EEG patterns is under preparation. Conclusions Amendatory scoring rules of those EEG patterns that are relevant for sleep scoring may contribute to increasing the reliability of visual sleep scoring and to support the development of standardized algorithms for computerized sleep analysis. Correspondence: Andrea Rodenbeck, Department of Psychiatry, University Go Tel.: +49-551-39 6947, Fax: +49-551-39 3887, E-mail: arodenb@gwdg.de Received: 15.05.06/Accepted: 22.08.06  2006 Blackwell Verlag, Berlin ¨ttingen, von-Siebold-Str. 5, 37075 Go ¨ttingen, Germany},
author = {Rodenbeck, Andrea and Binder, Ralf and Geisler, Peter and Danker-Hopfe, Heidi and Lund, Reimer and Raschke, Friedhart and Wee{\ss}, Hans G{\"{u}}nther and Schulz, Hartmut},
doi = {10.1111/j.1439-054X.2006.00101.x},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Review of Sleep EEG patterns.pdf:pdf},
isbn = {1439-054X},
issn = {14329123},
journal = {Somnologie},
keywords = {Alpha, theta, delta, vertex waves,K-complexes,Sawtooth waves,Scoring,Sleep EEG,Sleep spindles},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {4},
pages = {159--175},
title = {{A review of sleep EEG patterns. Part I: A compilation of amended rules for their visual recognition according to Rechtschaffen and Kales}},
volume = {10},
year = {2006}
}
@article{Thakor2004,
abstract = {entropy, complexity measure, information processing, fractal dimension applications I Abstract Quantitative electroencephalogram (qEEG) plays a significant role in EEG-based clinical diagnosis and studies of brain function. In past decades, various qEEG methods have been extensively studied. This article provides a detailed review of the advances in this field. qEEG methods are generally classified into linear and nonlinear approaches. The traditional qEEG approach is based on spectrum analysis, which hypothesizes that the EEG is a stationary process. EEG signals are nonstationary and nonlinear, especially in some pathological conditions. Various time-frequency representations and time-dependent measures have been proposed to address those transient and irregular events in EEG. With regard to the nonlinearity of EEG, higher order statistics and chaotic measures have been put forward. In characterizing the interactions across the cerebral cortex, an information theory-based measure such as mutual information is applied. To improve the spatial resolution, qEEG analysis has also been combined with medical imaging technology (e.g., CT, MR, and PET). With these advances, qEEG plays a very important role in basic research and clinical studies of brain injury, neurological disorders, epilepsy, sleep studies and consciousness, and brain function.},
author = {Thakor, N. V. and Tong, S.},
doi = {10.1146/annurev.bioeng.5.040202.121601},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Advances in Quantitative EEG Analysis.pdf:pdf},
isbn = {1523-9829 (Print)$\backslash$r1523-9829 (Linking)},
issn = {1523-9829},
journal = {Annual Review of Biomedical Engineering},
keywords = {complexity measure,eeg analysis,entropy,fractal dimension,information processing,signal processing,spectra,time-frequency analysis},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {1},
pages = {453--495},
pmid = {15255777},
title = {{Advances in Quantitative Electroencephalogram Analysis Methods}},
url = {http://www.annualreviews.org/doi/10.1146/annurev.bioeng.5.040202.121601},
volume = {6},
year = {2004}
}
@article{Puce2017,
abstract = {Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.},
author = {Puce, Aina and H{\"{a}}m{\"{a}}l{\"{a}}inen, Matti S.},
doi = {10.3390/brainsci7060058},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/A review of issues related to data acquisition in EEG analysis.pdf:pdf},
isbn = {1812650221},
issn = {20763425},
journal = {Brain Sciences},
keywords = {Artifacts,Data acquisition,Data analysis,EEG,MEG,Reference electrode,Sensor space,Source space},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {6},
pmid = {28561761},
title = {{A review of issues related to data acquisition and analysis in EEG/MEG studies}},
volume = {7},
year = {2017}
}
@article{Othman2005,
abstract = {This study introduces a new method for detecting, sorting, and localizing spikes from multiunit EEG recordings. The method combines the wavelet transform, which localizes distinctive spike features, with Super-Paramagnetic Clustering (SPC) algorithm, which allows automatic classification of the data without assumptions such as low variance or Gaussian distributions. Moreover, the method is capable of setting amplitude thresholds for spike detection. The method makes use of several real EEG data sets, and accordingly the spikes are detected, clustered and their times were detected.},
author = {Othman, MZ and Shaker, MM and Abdullah, MF},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/EEG spikes detection.pdf:pdf},
journal = {Periodical style—Accepted for {\ldots}},
keywords = {eeg spike detection,eeg time localizations,paramagnetic algorithm,super-,wavelet transform},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {9},
pages = {147--150},
title = {{EEG Spikes Detection, Sorting, and Localization}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.193.3615{\&}rep=rep1{\&}type=pdf},
volume = {1},
year = {2005}
}
@article{Shih2012,
abstract = {Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography- based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. {\textcopyright} 2012 Mayo Foundation for Medical Education and Research.},
author = {Shih, Jerry J. and Krusienski, Dean J. and Wolpaw, Jonathan R.},
doi = {10.1016/j.mayocp.2011.12.008},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/BCI in Medicine.pdf:pdf},
isbn = {0025-6196},
issn = {00256196},
journal = {Mayo Clinic Proceedings},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {3},
pages = {268--279},
pmid = {22325364},
publisher = {Elsevier Inc.},
title = {{Brain-computer interfaces in medicine}},
url = {http://dx.doi.org/10.1016/j.mayocp.2011.12.008},
volume = {87},
year = {2012}
}
@article{Cole2017,
abstract = {Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume that the oscillations are sinusoidal. While these approaches have proven fruitful, we show here that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Instead of being a nuisance, we discuss how these nonsinusoidal features may provide crucial and so far overlooked physiological information related to neural communication, computation, and cognition.},
author = {Cole, Scott R. and Voytek, Bradley},
doi = {10.1016/j.tics.2016.12.008},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Brain Oscillations and the importance of waveform shapes.pdf:pdf},
isbn = {1879-307X (Electronic) 1364-6613 (Linking)},
issn = {1879307X},
journal = {Trends in Cognitive Sciences},
keywords = {nonsinusoidal,oscillation,phase–amplitude coupling,shape,waveform},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {137--149},
pmid = {28063662},
publisher = {Elsevier Ltd},
title = {{Brain Oscillations and the Importance of Waveform Shape}},
url = {http://dx.doi.org/10.1016/j.tics.2016.12.008},
volume = {21},
year = {2017}
}
@book{Thakor2009,
abstract = {This authoritative volume provides an overview of basic and advanced techniques used in quantitative EEG (qEEG) analysis. The book provides a wide range of mathematical tools used in qEEG, from single channel discriptors to the interactions among multi-channel EEG analysis. Moreover, you find coverage of the latest and most popular application in the field, including mental and neurological disease detection/monitoring, physiological and cognitive phenomena research, and fMRI.},
author = {Thakor, N.V.},
publisher = {Artech House Series Engineering in Medicine and Biology},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Shanbao Tong, Nitish V. Thankor-Quantitative EEG Analysis Methods and Clinical Applications (Engineering in Medicine {\&} Biology) (2009).pdf:pdf},
isbn = {9781596932043},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {440},
title = {{Quantitative EEG analysis methods and clinical applications}},
url = {http://books.google.com/books?hl=en{\&}lr={\&}id=joodLUTyIEYC{\&}oi=fnd{\&}pg=PR13{\&}dq=quantitative+eeg+analysis+methods+and+clinical+applications{\&}ots=6uI5LTgY73{\&}sig=RR6I98VkT9RwrnrfNEIloSKkQlU},
year = {2009}
}
@article{Mognon2011,
abstract = {A successful method for removing artifacts from electroencephalogram ({\{}EEG{\}}) recordings is Independent Component Analysis ({\{}ICA{\}}), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm ({\{}ADJUST{\}}) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different {\{}EEG{\}} dataset shows that (1) {\{}ADJUST{\}}'s classification of independent components largely matches a manual one by experts (agreement on 95.2{\%} of the data variance), and (2) Removal of the artifacted components detected by {\{}ADJUST{\}} leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that {\{}ADJUST{\}} provides a fast, efficient, and automatic way to use {\{}ICA{\}} for artifact removal.},
author = {Mognon, Andrea and Jovicich, Jorge and Bruzzone, Lorenzo and Buiatti, Marco},
doi = {10.1111/j.1469-8986.2010.01061.x},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Adjust.pdf:pdf},
isbn = {1469-8986},
issn = {00485772},
journal = {Psychophysiology},
keywords = {Automatic classification,EEG artefacts,EEG artifacts,Electroencephalography,Event-related potentials,Independent component analysis,Ongoing brain activity,Thresholding},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {229--240},
pmid = {20636297},
title = {{ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features}},
volume = {48},
year = {2011}
}
@article{Fulcher2014,
abstract = {A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.},
archivePrefix = {arXiv},
arxivId = {1401.3531},
author = {Fulcher, Ben D. and Jones, Nick S.},
doi = {10.1109/TKDE.2014.2316504},
eprint = {1401.3531},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Time Series Classification.pdf:pdf},
isbn = {1041-4347},
issn = {10414347},
journal = {IEEE Transactions on Knowledge and Data Engineering},
keywords = {Time-series analysis,classification,data mining},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {12},
pages = {3026--3037},
title = {{Highly comparative feature-based time-series classification}},
volume = {26},
year = {2014}
}
@article{Nevado-Holgado2012,
abstract = {In this paper we propose that the dynamic evolution of EEG activity during epileptic seizures may be characterised as a path through parameter space of a neural mass model, reflecting gradual changes in underlying physiological mechanisms. Previous theoretical studies have shown how boundaries in parameter space of the model (so-called bifurcations) correspond to transitions in EEG waveforms between apparently normal, spike and wave and subsequently poly-spike and wave activity. In the present manuscript, we develop a multi-objective genetic algorithm that can estimate parameters of an underlying model from clinical data recordings. A standard approach to this problem is to transform both clinical data and model output into the frequency domain and then choose parameters that minimise the difference in their respective power spectra. Instead in the present manuscript, we estimate parameters in the time domain, their choice being determined according to the best fit obtained between the model output and specific features of the observed EEG waveform. This results in an approximate path through the bifurcation plane of the model obtained from clinical data. We present comparisons of such paths through parameter space from separate seizures from an individual subject, as well as between different subjects. Differences in the path reflect subtleties of variation in the dynamics of EEG, which at present appear indistinguishable using standard clinical techniques. ?? 2011 Elsevier Inc.},
archivePrefix = {arXiv},
arxivId = {0-387-31073-8},
author = {Nevado-Holgado, Alejo J. and Marten, Frank and Richardson, Mark P. and Terry, John R.},
doi = {10.1016/j.neuroimage.2011.08.111},
eprint = {0-387-31073-8},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Characterizing the dynamic EEG.pdf:pdf},
isbn = {1053-8119},
issn = {10538119},
journal = {NeuroImage},
keywords = {Bifurcation analysis,Multi-objective genetic algorithm,Neural mass model,Nonlinear dynamics,Nonlinear parameter estimation,Time-domain estimation},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {3},
pages = {2374--2392},
pmid = {21945471},
publisher = {Elsevier Inc.},
title = {{Characterising the dynamics of EEG waveforms as the path through parameter space of a neural mass model: Application to epilepsy seizure evolution}},
url = {http://dx.doi.org/10.1016/j.neuroimage.2011.08.111},
volume = {59},
year = {2012}
}
@article{Acharya2013,
abstract = {Epilepsy is an electrophysiological disorder of the brain, characterized by recurrent seizures. Electroencephalogram (EEG) is a test that measures and records the electrical activity of the brain, and is widely used in the detection and analysis of epileptic seizures. However, it is often difficult to identify subtle but critical changes in the EEG waveform by visual inspection, thus opening up a vast research area for biomedical engineers to develop and implement several intelligent algorithms for the identification of such subtle changes. Moreover, the EEG signals are nonlinear and non-stationary in nature, which contribute to further complexities related to their manual interpretation and detection of normal and abnormal (interictal and ictal) activities. Hence, it is necessary to develop a Computer Aided Diagnostic (CAD) system to automatically identify the normal and abnormal activities using minimum number of highly discriminating features in classifiers. It has been found that nonlinear features are able to capture the complex physiological phenomena such as abrupt transitions and chaotic behavior in the EEG signals. In this review, we discuss various feature extraction methods and the results of different automated epilepsy stage detection techniques in detail. We also briefly present the various open ended challenges that need to be addressed before a CAD based epilepsy detection system can be set-up in a clinical setting. {\textcopyright} 2013 Elsevier B.V. All rights reserved.},
author = {Acharya, U. Rajendra and {Vinitha Sree}, S. and Swapna, G. and Martis, Roshan Joy and Suri, Jasjit S.},
doi = {10.1016/j.knosys.2013.02.014},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Acharya et al. - 2013 - Automated EEG analysis of epilepsy A review.pdf:pdf},
isbn = {0950-7051},
issn = {09507051},
journal = {Knowledge-Based Systems},
keywords = {Classification,EEG,Epilepsy,Fractal dimension,Higher order spectra,Ictal,Interictal,Nonlinear,Recurrence plot},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {147--165},
publisher = {Elsevier B.V.},
title = {{Automated EEG analysis of epilepsy: A review}},
url = {http://dx.doi.org/10.1016/j.knosys.2013.02.014},
volume = {45},
year = {2013}
}
@book{Ziegel2004,
abstract = {To add yet another volume to the already numerous texts on statistics might seem to be an unwarranted exercise, yet the fact remains that many highly competent scientists are woefully ignorant of even the most elementary statistical methods. It is even more astonishing that analytical chemists, who practise one of the most quantitative of all sciences, are no more immune than others to this dangerous, but entirely curable, affliction. It is hoped, therefore, that this book will benefit analytical scientists who wish to design and conduct their experiments properly, and extract as much information from the results as they legitimately can. It is intended to be of value to the rapidly growing number of students specialising in analytical chemistry, and to those who use analytical methods routinely in every- day laboratory work. There},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Ziegel, Eric R},
booktitle = {Technometrics},
doi = {10.1198/tech.2004.s248},
eprint = {arXiv:1011.1669v3},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Statistics and Chemometrics for Analytical Chemistry.pdf:pdf},
isbn = {9780273730422},
issn = {0040-1706},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {4},
pages = {498--499},
pmid = {13810572},
title = {{Statistics and Chemometrics for Analytical Chemistry}},
url = {http://www.tandfonline.com/doi/abs/10.1198/tech.2004.s248},
volume = {46},
year = {2004}
}
@article{Ramadan2017,
abstract = {Brain Computer Interface (BCI) is defined as a combination of hardware and software that allows brain activities to control external devices or even computers. The research in this field has attracted academia and industry alike. The objective is to help severely disabled people to live their life as regular persons as much as possible. Some of these disabilities are categorized as neurological neuromuscular disorders. A BCI system goes through many phases including preprocessing, feature extraction, signal classifications, and finally control. Large body of research are found at each phase and this might confuse researchers and BCI developers. This article is a review to the state-of-the-art work in the field of BCI. The main focus of this review is on the Brain control signals, their types and classifications. In addition, this survey reviews the current BCI technology in terms of hardware and software where the most used BCI devices are described as well as the most utilized software platforms are explained. Finally, BCI challenges and future directions are stated. Due to the limited space and large body of literature in the field of BCI, another two review articles are planned. One of these articles reviews the up-to-date BCI algorithms and techniques for signal processing, feature extraction, signals classification, and control. Another article will be dedicated to BCI systems and applications. The three articles are written as base and guidelines for researchers and developers pursue the work in the field of BCI.},
author = {Ramadan, Rabie A. and Vasilakos, Athanasios V.},
doi = {10.1016/j.neucom.2016.10.024},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Control Signal Review.pdf:pdf},
issn = {18728286},
journal = {Neurocomputing},
keywords = {BCI,BCI challenges,BCI signals,BCI technology,Brain computer interface,Future directions,Hardware,Software},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {April 2017},
pages = {26--44},
title = {{Brain computer interface: control signals review}},
volume = {223},
year = {2017}
}
@article{Nam2010,
abstract = {Despite recent advances in brain-computer interface (BCI) development, system usability still remains a large oversight. The goal of this study was to investigate the usability of a P300-based BCI system, P300 Speller, by assessing how background noise and interface color contrast affect user performance and BCI usage preference. Fifteen able-bodied participants underwent a 2 (low and high interface color contrast) × 3 (low, medium, and high background noise level) within-subjects design experiment, in which participants were asked to type six 10-character phrases in the P300 Speller paradigm. The overall accuracy in the study was 80.2{\%}. Participants showed higher accuracy, higher information transfer rate, bigger amplitude, and smaller latency in the high interface color contrast condition than in the low contrast condition. Participants had better performance in the noisy condition than in the quiet condition, but the background noise effects were not statistically significant in the present study. These results should give some insight to the real-world applicability of the current P300 Speller as a nonmuscular communication system, especially for individuals with severe neuromuscular disabilities. {\textcopyright} Taylor {\&} Francis Group, LLC.},
author = {Nam, Chang S. and Li, Yueqing and Johnson, Steve},
doi = {10.1080/10447311003781326},
isbn = {1044-7318},
issn = {10447318},
journal = {International Journal of Human-Computer Interaction},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {may},
number = {6},
pages = {621--637},
publisher = {Taylor {\&} Francis Group},
title = {{Evaluation of P300-based brain-computer interface in real-world contexts}},
url = {http://www.tandfonline.com/doi/abs/10.1080/10447311003781326},
volume = {26},
year = {2010}
}
@inproceedings{TomohiroMadarame2008,
abstract = {The objective of this research was to develop a brain computer interface (BCI) communication device for amyotrophic lateral sclerosis (ALS) patients. The device was designed to meet the needs of ALS patients, and to be used at a clinical level. Initial tests were performed by ALS patients, and the result was accounted for in the experimental production of the communication device. Lastly, the device was evaluated by able-bodied examinees and ALS patients. For able-bodied examinees, the device scored a high rate of correct sessions. When an ALS patient was the user, the correction rate was not as well, but it would have scored highly if a correct parameter was chosen. {\&}copy; 2008 IEEE.},
author = {Madarame, Tomohiro and Tanaka, Hisaya and Inoue, Takenobu and Kamata, Minoru and Shino, Motoki},
booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
doi = {10.1109/ICSMC.2008.4811654},
isbn = {978-1-4244-2383-5},
issn = {1062922X},
keywords = {Amyotrophic lateral sclerosis,Brain-computer interface,Communication aid device,Event related potentials,P300},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {oct},
pages = {2401--2406},
publisher = {IEEE},
title = {{The development of a brain computer interface device for amyotrophic lateral sclerosis patients}},
url = {http://ieeexplore.ieee.org/document/4811654/},
year = {2008}
}
@article{McCane2015,
abstract = {Objective: Brain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. Methods: Sixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0-25)). Each subject attended to a target item as the items in a 6. ×. 6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. Results: BCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). Conclusions: The differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). Significance: P300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.},
archivePrefix = {arXiv},
arxivId = {15334406},
author = {McCane, Lynn M and Heckman, Susan M and McFarland, Dennis J and Townsend, George and Mak, Joseph N and Sellers, Eric W and Zeitlin, Debra and Tenteromano, Laura M and Wolpaw, Jonathan R and Vaughan, Theresa M},
doi = {10.1016/j.clinph.2015.01.013},
eprint = {15334406},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/McCane et al. - 2015 - P300-based brain-computer interface (BCI) event-related potentials (ERPs) People with amyotrophic lateral scleros.pdf:pdf},
isbn = {1388-2457},
issn = {18728952},
journal = {Clinical Neurophysiology},
keywords = {Alternative and augmentative communication (AAC),Amyotrophic lateral sclerosis (ALS),Brain-computer interface (BCI),Brain-machine interface (BMI),Electroencephalography (EEG),Event-related potentials (ERP)},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {nov},
number = {11},
pages = {2124--2131},
pmid = {25703940},
publisher = {NIH Public Access},
title = {{P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25703940 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4529383},
volume = {126},
year = {2015}
}
@article{Novak2018,
abstract = {This paper presents a new approach to benchmarking brain-computer interfaces (BCIs) outside the lab. A computer game was created that mimics a real-world application of assistive BCIs, with the main outcome metric being the time needed to complete the game. This approach was used at the Cybathlon 2016, a competition for people with disabilities who use assistive technology to achieve tasks. The paper summarizes the technical challenges of BCIs, describes the design of the benchmarking game, then describes the rules for acceptable hardware, software and inclusion of human pilots in the BCI competition at the Cybathlon. The 11 participating teams, their approaches, and their results at the Cybathlon are presented. Though the benchmarking procedure has some limitations (for instance, we were unable to identify any factors that clearly contribute to BCI performance), it can be successfully used to analyze BCI performance in realistic, less structured conditions. In the future, the parameters of the benchmarking game could be modified to better mimic different applications (e.g. the need to use some commands more frequently than others). Furthermore, the Cybathlon has the potential to showcase such devices to the general public.},
author = {Novak, Domen and Sigrist, Roland and Gerig, Nicolas J. and Wyss, Dario and Bauer, Ren{\'{e}} and G{\"{o}}tz, Ulrich and Riener, Robert},
doi = {10.3389/fnins.2017.00756},
issn = {1662-453X},
journal = {Frontiers in Neuroscience},
keywords = {Benchmark testing,Brain-Computer Interfaces,Electroencephalography,competition,machine learning},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {jan},
pages = {15},
publisher = {Frontiers},
title = {{Benchmarking Brain-Computer Interfaces Outside the Laboratory: The Cybathlon 2016}},
url = {http://journal.frontiersin.org/article/10.3389/fnins.2017.00756/full},
volume = {11},
year = {2018}
}
@article{Chavarriaga2017,
abstract = {AbstractResearch in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of-concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that inhibit the achievement of this goal have already been identified. However, it is not clear how they affect the overall BCI performance or how they should be tackled. This is worsened by publication bias, where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research aven...},
author = {Chavarriaga, Ricardo and Fried-Oken, Melanie and Kleih, Sonja and Lotte, Fabien and Scherer, Reinhold},
doi = {10.1080/2326263X.2016.1263916},
issn = {2326-263X},
journal = {Brain-Computer Interfaces},
keywords = {BCI,EEG,artifacts,limitations,publication bias,signal processing,user training,user-centered design},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {apr},
number = {1-2},
pages = {60--73},
publisher = {Taylor {\&} Francis},
title = {{Heading for new shores! Overcoming pitfalls in BCI design}},
url = {https://www.tandfonline.com/doi/full/10.1080/2326263X.2016.1263916},
volume = {4},
year = {2017}
}
@book{Rao2013,
abstract = {Introduction -- Part I: Background -- Basic neuroscience -- Recording and stimulating the brain -- Signal processing -- Machine learning -- Part II: Putting it all together -- Building a BCI -- Part III: Major types of BCIs -- Invasive BCIs -- Semi-invasive BCIs -- Noninvasive BCIs -- BCIs that stimulate -- Part IV: Applications and ethics -- Application of BCIs -- Ethics of brain-computer interfacing -- Conclusion.},
author = {Rao, Rajesh P. N.},
isbn = {0521769418},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {319},
year = {2013},
publisher = {Cambridge University Press},
title = {{Brain-computer interfacing : an introduction}}
}
@article{Monajemi2017,
abstract = {In this study, we propose a novel method to investigate P300 variability over different trials. The method incorporates spatial correlation between EEG channels to form a cooperative coupled particle filtering method that tracks the P300 subcomponents, P3a and P3b, over trials. Using state space systems, the amplitude, latency, and width of each subcomponent are modeled as the main underlying parameters. With four electrodes, two coupled Rao-Blackwellised particle filter pairs are used to recursively estimate the system state over trials. A number of physiological constraints are also imposed to avoid generating invalid particles in the estimation process. Motivated by the bilateral symmetry of ERPs over the brain, the channels further share their estimates with their neighbors and combine the received information to obtain a more accurate and robust solution. The proposed algorithm is capable of estimating the P300 subcomponents in single trials and outperforms its non-cooperative counterpart.},
author = {Monajemi, Sadaf and Jarchi, Delaram and Ong, Sim Heng and Sanei, Saeid},
doi = {10.3390/e19050199},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Monajemi et al. - 2017 - Cooperative Particle Filtering for Tracking ERP Subcomponents from Multichannel EEG.pdf:pdf},
issn = {10994300},
journal = {Entropy},
keywords = {Cooperative particle filtering,Coupled RBPF,Event related potential (ERP),P300,Rao-Blackwellised particle filter (RBPF)},
mendeley-groups = {Entropy,Thesis,Thesis2},
month = {apr},
number = {5},
pages = {199},
publisher = {Multidisciplinary Digital Publishing Institute},
title = {{Cooperative particle filtering for tracking ERP subcomponents from multichannel EEG}},
url = {http://www.mdpi.com/1099-4300/19/5/199},
volume = {19},
year = {2017}
}
@misc{Polich2007,
abstract = {The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations. {\textcopyright} 2007 International Federation of Clinical Neurophysiology.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Polich, John},
booktitle = {Clinical Neurophysiology},
doi = {10.1016/j.clinph.2007.04.019},
eprint = {arXiv:1011.1669v3},
isbn = {1388-2457 (Print)$\backslash$r1388-2457 (Linking)},
issn = {13882457},
keywords = {Attention,ERP,Inhibition,Memory,Neuropsychology,Neurotransmitters,P300,P3a,P3b,Theory},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {oct},
number = {10},
pages = {2128--2148},
pmid = {17573239},
title = {{Updating P300: An integrative theory of P3a and P3b}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17573239 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC2715154 http://linkinghub.elsevier.com/retrieve/pii/S1388245707001897},
volume = {118},
year = {2007}
}
@article{Cui2016,
abstract = {The quantitative diagnosis of rolling bearing fault severity is particularly crucial to realize a proper maintenance decision. Aiming at the fault feature of rolling bearing, a novel double-dictionary matching pursuit (DDMP) for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity (LZC) index is proposed in this paper. In order to match the features of rolling bearing fault, the impulse time-frequency dictionary and modulation dictionary are constructed to form the double-dictionary by using the method of parameterized function model. Then a novel matching pursuit method is proposed based on the new double-dictionary. For rolling bearing vibration signals with different fault sizes, the signals are decomposed and reconstructed by the DDMP. After the noise reduced and signals reconstructed, the LZC index is introduced to realize the fault extent evaluation. The applications of this method to the fault experimental signals of bearing outer race and inner race with different degree of injury have shown that the proposed method can effectively realize the fault extent evaluation.},
author = {Cui, Lingli and Gong, Xiangyang and Zhang, Jianyu and Wang, Huaqing},
doi = {10.1016/j.jsv.2016.09.008},
issn = {10958568},
journal = {Journal of Sound and Vibration},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {dec},
pages = {372--388},
publisher = {Academic Press},
title = {{Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity}},
url = {http://www.sciencedirect.com/science/article/pii/S0022460X16304540?via{\%}3Dihub},
volume = {385},
year = {2016}
}
@article {Miller2010,
	author = {Miller, Kai J.},
	title = {Broadband Spectral Change: Evidence for a Macroscale Correlate of Population Firing Rate?},
	volume = {30},
	number = {19},
	pages = {6477--6479},
	year = {2010},
	doi = {10.1523/JNEUROSCI.6401-09.2010},
	publisher = {Society for Neuroscience},
	issn = {0270-6474},
	URL = {http://www.jneurosci.org/content/30/19/6477},
	eprint = {http://www.jneurosci.org/content/30/19/6477.full.pdf},
	journal = {Journal of Neuroscience}
}
@article{Lloyd-Sherlock2000,
abstract = {Population ageing is now recognised as a global issue of increasing importance, and has many implications for health care and other areas of social policy. However, these issues remain relatively under-researched, particularly in poorer countries, and there is a dearth of specific policy initiatives at the international level. For example, the 1994 International Conference on Population and Development agreed to 15 key principles for future policy, but none of these even make indirect mention of the aged (International Conference on Population and Development, 1995, Documents. Programme of action of the 1994 International Conference on Population and Development. Population and Development Review, 21(2), 437-461). This paper seeks to highlight some of the key issues arising from population ageing. It begins with a brief overview of international trends in demographic ageing, and considers the health needs of different groups of older people. It sketches out some implications for policy, paying particular attention to the financing and organisation of health services. The final part of the paper contains a discussion about how older people have been affected by, and have adapted to, processes of social, economic and political change. Given the wide scope of these concerns, it is not possible to discuss any issue in detail, and the paper does not claim to give the subject matter a comprehensive or global treatment. It must be stressed that patterns of ageing and their implications for policy are highly complex and variable, and, as such, great care should be taken in generalising between the experiences of different groups of older people, and between different settings. Copyright (C) 2000 Elsevier Science Ltd.},
author = {Lloyd-Sherlock, Peter},
doi = {10.1016/S0277-9536(00)00068-X},
isbn = {0277-9536},
issn = {02779536},
journal = {Social Science and Medicine},
keywords = {Ageing,Demography,Epidemiology,Financing,Policy,Poverty},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {sep},
number = {6},
pages = {887--895},
pmid = {10972432},
publisher = {Pergamon},
title = {{Population ageing in developed and developing regions: Implications for health policy}},
url = {http://www.sciencedirect.com/science/article/pii/S027795360000068X},
volume = {51},
year = {2000}
}
@article{Farzan2017,
abstract = {Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.},
author = {Farzan, Faranak and Atluri, Sravya and Frehlich, Matthew and Dhami, Prabhjot and Kleffner, Killian and Price, Rae and Lam, Raymond W. and Frey, Benicio N. and Milev, Roumen and Ravindran, Arun and McAndrews, Mary Pat and Wong, Willy and Blumberger, Daniel and Daskalakis, Zafiris J. and Vila-Rodriguez, Fidel and Alonso, Esther and Brenner, Colleen A. and Liotti, Mario and Dharsee, Moyez and Arnott, Stephen R. and Evans, Kenneth R. and Rotzinger, Susan and Kennedy, Sidney H.},
doi = {10.1038/s41598-017-07613-x},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Farzan et al. - 2017 - Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies insigh(2).pdf:pdf},
issn = {20452322},
journal = {Scientific Reports},
keywords = {Humanities and Social Sciences,Science,multidisciplinary},
mendeley-groups = {EEGWaveformAnalysis,Thesis2},
month = {dec},
number = {1},
pages = {7473},
pmid = {28785082},
publisher = {Nature Publishing Group},
title = {{Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: Insights from the canadian biomarker integration network in depression}},
url = {http://www.nature.com/articles/s41598-017-07613-x},
volume = {7},
year = {2017}
}
@article{Nicolas-Alonso2012,
abstract = {A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.},
archivePrefix = {arXiv},
arxivId = {10.3390/s120201211},
author = {Nicolas-Alonso, Luis Fernando and Gomez-Gil, Jaime},
doi = {10.3390/s120201211},
eprint = {s120201211},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Nicolas-Alonso, Gomez-Gil - 2012 - Brain Computer Interfaces, a Review.pdf:pdf},
isbn = {0308018032250},
issn = {14248220},
journal = {Sensors},
keywords = {Artifact,Brain-computer interface (BCI),Brain-machine interface,Collaborative sensor system,Electroencephalography (EEG),Neuroimaging,Rehabilitation},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {jan},
number = {2},
pages = {1211--1279},
pmid = {22438708},
primaryClass = {10.3390},
publisher = {Molecular Diversity Preservation International},
title = {{Brain computer interfaces, a review}},
url = {http://www.mdpi.com/1424-8220/12/2/1211/},
volume = {12},
year = {2012}
}
@article{Tong1995,
abstract = {An automatic analyser which characterizes, as well as detects, seismic phases can be a significant help in the interpretation of seismograms. Such an analyser has been constructed using concepts from work in artificial intelligence. A seismogram is recognized as a hierarchical structure of subunits and the features characterizing a phase are extracted via a structural analysis. The automatic analyser detects the onsets of phases, separates the relevant waveform segments, fits phase structures to standard wavelets, and generates a set of five parameters ( a quintuple') to characterize a detected phase. The whole analysis system requires just a few input parameters and can be accomplished swiftly enough to allow continuous real-time operation. The analysis procedure is adaptive, including updating of the estimate of the local frequency so that it can be applied to broad-band records with a wide range of frequency content. By coupling the analysis procedure with real-time filtering, very good results for phase detection and characterization can be achieved for weak distant events. The characterization of phase segments also provides useful information for the comparison of seismograms.},
author = {Tong, Cheng},
doi = {10.1111/j.1365-246X.1995.tb06900.x},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Tong - 1995 - Characterization of seismic phases-an automatic analyser for seismograms.pdf:pdf},
issn = {1365246X},
journal = {Geophysical Journal International},
keywords = {broad‐band seismology,phase detection,phase‐feature extraction,seismogram analysis},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {dec},
number = {3},
pages = {937--947},
publisher = {Oxford University Press},
title = {{Characterization of seismic phases—an automatic analyser for seismograms}},
url = {https://academic.oup.com/gji/article-lookup/doi/10.1111/j.1365-246X.1995.tb06900.x},
volume = {123},
year = {1995}
}
@article{Bremer1970,
abstract = {The development and application of an electronic system which is capable of automatically detecting the K-complexes, aperiodic waveforms found in sleep electroencephalograms, is described. The system can be used either on- or off-line.},
author = {Bremer, Gordon and Smith, Jack R. and Karacan, Ismet},
doi = {10.1109/TBME.1970.4502759},
isbn = {0018-9294 (Print)$\backslash$r0018-9294 (Linking)},
issn = {15582531},
journal = {IEEE Transactions on Biomedical Engineering},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
month = {oct},
number = {4},
pages = {314--323},
pmid = {5518827},
title = {{Automatic Detection of the K-Complex in Sleep Electroencephalograms}},
url = {http://ieeexplore.ieee.org/document/4502759/},
volume = {BME-17},
year = {1970}
}
@article{Portain1990,
author = {Portain, Dominic},
doi = {10.1007/BF03192151},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Thesis.Comparision of Template Based models.pdf:pdf},
issn = {0178-7888},
journal = {Mycotoxin research},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {100},
pmid = {23605451},
title = {{Master thesis.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/21365933},
volume = {6},
year = {1990}
}
@article{Akmal2016,
author = {Akmal, Muhammad and Jochumsen, Mads and Navid, Muhammad Samran and Sha, Muhammad and Muhammad, Syed and Zaidi, Tahir and Taylor, Denise},
doi = {10.1007/978-3-319-33747-0},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Universal matched filter.pdf:pdf},
isbn = {978-3-319-33746-3},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {275--282},
title = {{Advances in Neural Networks}},
url = {http://link.springer.com/10.1007/978-3-319-33747-0},
volume = {54},
year = {2016}
}
@article{Wright2011,
author = {Wright, Tom and Nilsson, Josefin and Westall, Carol},
doi = {10.1097/WNP.0b013e3182273351},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Isolating Visual Evoked Responses Comparing algorithms.pdf:pdf},
issn = {07360258},
journal = {Journal of Clinical Neurophysiology},
keywords = {28,404,411,adrian,clinical,identified in 1934,j clin neurophysiol 2011,lectrical potentials originating from,signal detection,the cortex in response,to,visual evoked potential,visual stimulation were originally},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {4},
pages = {404},
title = {{Isolating Visual Evoked Responses Comparing Signal Identification Algorithms}},
url = {http://journals.lww.com/clinicalneurophys/Abstract/2011/08000/Isolating{\_}Visual{\_}Evoked{\_}Responses{\_}Comparing{\_}Signal.15.aspx},
volume = {28},
year = {2011}
}
@article{Zhang2011,
author = {Zhang, Dandan and Luo, Yuejia},
doi = {10.1109/BMEI.2011.6098464},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/The P1 Latency of Single Trial ERPS estimated by pick picking.pdf:pdf},
isbn = {9781424493524},
journal = {Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011},
keywords = {event-related potential,peak latency,peak-picking},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {882--886},
title = {{The P1 latency of single-trial ERPs estimated by two peak-picking strategies}},
volume = {2},
year = {2011}
}
@article{Lafuente2017,
author = {Lafuente, Victor and Gorriz, J. M. and Ramirez, J. and Murcia, F. J.Martinez and Salas, D. and Segovia, F.},
doi = {10.1109/CSCI.2016.0237},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Match Filtering.pdf:pdf},
isbn = {9781509055104},
journal = {Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016},
keywords = {Artifacts,Classification,EEG,ERP,ICA,Matched filters,P300,Wiener filter},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {1261--1265},
title = {{On the Use of Match Filtering for the P300 Detection}},
year = {2017}
}
@article{Marathe2014,
abstract = {Patterns of neural data obtained from electroencephalography (EEG) can be classified by machine learning techniques to increase human-system performance. In controlled laboratory settings this classification approach works well; however, transitioning these approaches into more dynamic, unconstrained environments will present several significant challenges. One such challenge is an increase in temporal variability in measured behavioral and neural responses, which often results in suboptimal classification performance. Previously, we reported a novel classification method designed to account for temporal variability in the neural response in order to improve classification performance by using sliding windows in hierarchical discriminant component analysis (HDCA), and demonstrated a decrease in classification error by over 50{\%} when compared to the standard HDCA method (Marathe et al., 2013). Here, we expand upon this approach and show that embedded within this new method is a novel signal transformation that, when applied to EEG signals, significantly improves the signal-to-noise ratio and thereby enables more accurate single-trial analysis. The results presented here have significant implications for both brain-computer interaction technologies and basic science research into neural processes.},
author = {Marathe, Amar R. and Ries, Anthony J. and McDowell, Kaleb},
doi = {10.1109/TNSRE.2014.2304884},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Sliding HDCA single trial eeg classification to overcome temporal variability.pdf:pdf},
issn = {15344320},
journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
keywords = {Brain-computer interface (BCI),Temporal variability,electroencephalography (EEG),hierarchical discriminant component analysis (HDCA,real-world environment,single-trial,sliding HDCA},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {201--211},
pmid = {24608681},
title = {{Sliding HDCA: Single-trial eeg classification to overcome and quantify temporal variability}},
volume = {22},
year = {2014}
}
@article{Weeda2012,
abstract = {The amplitude and latency of single-trial EEG/MEG signals may provide valuable information concerning human brain functioning. In this article we propose a new method to reliably estimate single-trial amplitude and latency of EEG/MEG signals. The advantages of the method are fourfold. First, no a-priori specified template function is required. Second, the method allows for multiple signals that may vary independently in amplitude and/or latency. Third, the method is less sensitive to noise as it models data with a parsimonious set of basis functions. Finally, the method is very fast since it is based on an iterative linear least squares algorithm. A simulation study shows that the method yields reliable estimates under different levels of latency variation and signal-to-noise ratio{\~{O}}s. Furthermore, it shows that the existence of multiple signals can be correctly determined. An application to empirical data from a choice reaction time study indicates that the method describes these data accurately.},
author = {Weeda, Wouter D. and Grasman, Raoul P P P and Waldorp, Lourens J. and van de Laar, Maria C. and van der Molen, Maurits W. and Huizenga, Hilde M.},
doi = {10.1371/journal.pone.0038292},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Fast and reliable method for waveform amplitude and latency estimation.PDF:PDF},
issn = {19326203},
journal = {PLoS ONE},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {6},
pages = {1--12},
pmid = {22761672},
title = {{A fast and reliable method for simultaneous waveform, amplitude and latency estimation of single-trial EEG/MEG data}},
volume = {7},
year = {2012}
}
@article{Salimi-Khorshidi2008,
abstract = {Objective: Designing a reliable and accurate brain-computer interface (BCI) is one of the most challenging fields in biomedical signal processing. To achieve this goal, different methods have been adopted in different blocks of a typical BCI system (i.e., in preprocessing, feature extraction, feature classification and feature selection blocks). Since BCI's speed plays a crucial role in its success in real-life applications, using mathematically simple techniques with accurate and reliable performance can improve this aspect of BCI systems' design. Methods and materials: In this paper, a new method is introduced, which combines information from different classic time series similarity measures, using a simple fuzzy fusion framework. This method is accurate and reliable in P300 (a positive event-related component occurring 300 ms after stimulus onset) detection. This framework is used to combine two computationally simple signal detection methods: "peak picking" and "template matching". Fusion takes place in the last step (decision-making step) by means of a fuzzy rule-base. Results and conclusions: Compared to similar works on electroencephalogram-based (EEG-based) BCI datasets, in spite of being computationally simple, this new technique's performance is comparable to very complicated methods, like support vector machines. This research indicates that, using both spatial and temporal information content of EEG trials (from all electrodes or a subset of them), even under a non-complicated mathematical framework can yield an accurate and powerful classification. {\textcopyright} 2008 Elsevier B.V. All rights reserved.},
author = {Salimi-Khorshidi, Gholamreza and Nasrabadi, Ali Motie and Golpayegani, Mohammadreza Hashemi},
doi = {10.1016/j.artmed.2008.06.002},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/fusion of classic p300 detection methods.pdf:pdf},
isbn = {0933-3657},
issn = {09333657},
journal = {Artificial Intelligence in Medicine},
keywords = {Brain-computer interface (BCI),Classification,Event-related potentials (ERP),Fuzzy information fusion,Fuzzy rule-base,P300,Peak picking,Template matching},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {3},
pages = {247--259},
pmid = {18703323},
title = {{Fusion of classic P300 detection methods' inferences in a framework of fuzzy labels}},
volume = {44},
year = {2008}
}
@article{Zhong2015,
author = {Zhong, Ming},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Novel Spectral Representations and Sparsity-Based Methods for Shape Modeling and Analysis.pdf:pdf},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {May},
title = {{Novel Spectral Representations and Sparsity-Based Methods for Shape Modeling and Analysis}},
year = {2015}
}
@article{Paper2007,
author = {Paper, Conference},
doi = {10.1007/978-3-540-68017-8},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Modifying{\_}the{\_}Classic{\_}Template{\_}Matching{\_}Technique{\_}.pdf:pdf},
isbn = {978-3-540-68016-1},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {January},
title = {{3rd Kuala Lumpur International Conference on Biomedical Engineering 2006}},
url = {http://link.springer.com/10.1007/978-3-540-68017-8},
volume = {15},
year = {2007}
}
@article{Langen2012,
abstract = {Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. To this end, many computational methods have been devel-oped for both the detection and prediction of epileptic seizures. Techniques derived from linear/nonlinear analysis, chaos, information theory, morphological analysis, model-based analysis, all present different advantages, disadvantages, and limitations. Recently, there is the notion of selecting and combining the most robust features from different methods for revealing various signals' characteristics and making more reliable assumptions. Finally, intelligent classifiers are employed in order to distinguish epileptic state out of normal states. This chapter reviews the most widely adopted algorithms for the detection and prediction of epileptic seizures, emphasizing on information theory based and entropy indices. Each method's accuracy has been evaluated through performance measures, assessing the ability of automatic seizure detection/ prediction.},
author = {Langen, Karl-josef},
doi = {10.1007/7657},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Methods for seizure detection and prediction.pdf:pdf},
isbn = {1493912976},
issn = {08932336},
journal = {Molecular Imaging in the Clinical Neurosciences},
keywords = {11 c-methionine,18 f-fluorodeox-,18 f-fluoroethyltyrosine,biopsy,cerebral glioma,guidance,molecular imaging,positron emission tomography,proliferation,radiolabeled amino acids,tumor extent,yglucose},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {May},
pages = {345--357},
pmid = {19959009},
title = {{Molecular Imaging of Brain Tumors}},
year = {2012}
}
@article{Takeda2010,
abstract = {We propose a generalized method to estimate waveforms common across trials from electroencephalographic (EEG) data. From single/multi-channel EEGs, the proposed method estimates the number of waveforms common across trials, their delays in individual trials, and all of the waveforms. After verifying the performance of this method by a number of simulation tests with artificial EEGs, we apply it to EEGs during a Go/NoGo task. This method can be used in general situations where the number and the delays of EEG waveforms common across trials are unknown. ?? 2010 Elsevier Inc.},
author = {Takeda, Yusuke and Sato, Masa Aki and Yamanaka, Kentaro and Nozaki, Daichi and Yamamoto, Yoshiharu},
doi = {10.1016/j.neuroimage.2010.02.002},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/A generalized method to estimate waveforms common across trials from EEG.pdf:pdf},
isbn = {1095-9572 (Electronic)$\backslash$n1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
keywords = {EEG waveforms common across trials,Electroencephalography (EEG),Go/NoGo task},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {629--641},
pmid = {20149878},
title = {{A generalized method to estimate waveforms common across trials from EEGs}},
volume = {51},
year = {2010}
}
@book{Dengler2004,
author = {Dengler, Reinhard and Krampfl, Klaus},
booktitle = {Handbook of Clinical Neurophysiology},
doi = {10.1016/S1567-4231(04)04015-8},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Evoked Potentials in Amyotrophic Lateral Sclerosis.pdf:pdf},
issn = {1567-4231},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {295--303},
publisher = {Elsevier B.V.},
title = {{Evoked potentials in amyotrophic lateral sclerosis}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S1567423104040158},
volume = {4},
year = {2004}
}
@article{Jaskowski2000,
abstract = {This study investigated the validity of procedures for estimating the P3 complex in single trials. In "pseudo-real" simulations of the N1-P2 complex of the occipital visual-evoked potential, M{\"{o}}cks, K{\"{o}}hler, Gasser, and Pham (1988) had reported that their maximum-likelihood method (Pham, M{\"{o}}cks, K{\"{o}}hler, {\&} Gasser, 1987) performed better than Woody's (1967) method. Using pseudo-real simulations of auditory oddball data, we wanted to know whether this finding also held true for the P3 complex. The performance of three methods was studied: peak picking, Woody's method, and Pham et al.'s method (as well as an extension of this latter method). Performance of all methods critically depended on the signal-to-noise ratio. There was some advantage for the more sophisticated methods, particularly when signal-to-noise ratios were realistic. "Good" trials may be selected by all methods, to improve the signal-to-noise ratio, but this selection entails the risk of bias. Further research should investigate whether these conclusions also hold true when the P3 complex consists of more than one component.},
author = {Ja{\v{s}}kowski, Piotr and Verleger, Rolf},
doi = {10.1111/1469-8986.3720153},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/An evalution of methods for p3 detection.pdf:pdf},
isbn = {0048-5772 (Print)},
issn = {00485772},
journal = {Psychophysiology},
keywords = {Cross-covariance,Event-related potential,Maximum likelihood,P3(00),Simulations,Single-trial estimation},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {2},
pages = {153--162},
pmid = {10731766},
title = {{An evaluation of methods for single-trial estimation of P3 latency}},
volume = {37},
year = {2000}
}
@article{Ouyang2017,
abstract = {The intra-subject variability (ISV) in brain responses during cognitive processing across experimental trials has been recognised as an important facet of neural functionality reflecting an intrinsic neurophysiological characteristic of the brain. In recent decades, ISV in behaviour has been found to be significantly associated with cognitive functioning varying across individuals, development, ages, and pathological conditions. Event-related potentials (ERPs) measured in single trials are important tools for characterizing ISV at the neural level. However, due to the overlapping spectra of noise and signals, the retrieval of information from single-trial ERPs related to cognitive processing has been a challenge. We review the major problems that researchers face in the estimation of ISV in single-trial ERPs. Then, we present an extensive evaluation of several methods of single-trial latency estimation based on both simulated and real data. The relationships of ISV in ERPs and reaction times are compared between the different single-trial methods to assess their relative efficiency in predicting task performance from neural signals. The pros and cons of the methods are discussed.},
author = {Ouyang, Guang and Hildebrandt, Andrea and Sommer, Werner and Zhou, Changsong},
doi = {10.1016/j.neubiorev.2017.01.023},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Exploiting ISV single trial in P3.pdf:pdf},
issn = {18737528},
journal = {Neuroscience and Biobehavioral Reviews},
keywords = {Intra-subject variability,Method evaluation,Single-trial ERP latency estimation,Trial-to-trial latency variability},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
pages = {1--21},
publisher = {Elsevier Ltd},
title = {{Exploiting the intra-subject latency variability from single-trial event-related potentials in the P3 time range: A review and comparative evaluation of methods}},
url = {http://dx.doi.org/10.1016/j.neubiorev.2017.01.023},
volume = {75},
year = {2017}
}
@article{Wang2001,
author = {Wang, K and Begleiter, H and Porjesz, B},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Warp Avaraging.pdf:pdf},
isbn = {1388-2457},
journal = {Clinical Neurophysiology},
keywords = {dynamic time warping,event-related potentials,single trials},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {10},
pages = {1917--1924},
title = {{Warp-averaging event-related potentials}},
volume = {112},
year = {2001}
}
@article{ChandranKS2016,
abstract = {Signals recorded from the brain often show rhythmic patterns at different frequencies, which are tightly coupled to the external stimuli as well as the internal state of the subject. In addition, these signals have very transient structures related to spiking or sudden onset of a stimulus, which have durations not exceeding tens of milliseconds. Further, brain signals are highly nonstationary because both behavioral state and external stimuli can change on a short time scale. It is therefore essential to study brain signals using techniques that can represent both rhythmic and transient components of the signal, something not always possible using standard signal processing techniques such as short time fourier transform, multitaper method, wavelet transform, or Hilbert transform. In this review, we describe a multiscale decomposition technique based on an over-complete dictionary called matching pursuit (MP), and show that it is able to capture both a sharp stimulus-onset transient and a sustained gamma rhythm in local field potential recorded from the primary visual cortex. We compare the performance of MP with other techniques and discuss its advantages and limitations. Data and codes for generating all time-frequency power spectra are provided.},
author = {{Chandran KS}, S. and Mishra, A. and Shirhatti, V. and Ray, S.},
doi = {10.1523/JNEUROSCI.3633-15.2016},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Chandran KS et al. - 2016 - Comparison of Matching Pursuit Algorithm with Other Signal Processing Techniques for Computation of the Time.pdf:pdf},
issn = {0270-6474},
journal = {Journal of Neuroscience},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {12},
pages = {3399--3408},
pmid = {27013668},
title = {{Comparison of Matching Pursuit algorithm with other signal processing techniques for computation of the time-frequency power spectrum of brain signals}},
url = {http://www.jneurosci.org/cgi/doi/10.1523/JNEUROSCI.3633-15.2016},
volume = {36},
year = {2016}
}
@article{Casarotto2005,
abstract = { The aim of this article is to compute reliable templates of event-related potentials (ERPs) for homogeneous groups of subjects and to automatically quantify the morphological characteristics of the ERPs. We developed a method based on dynamic time warping (DTW). The method was applied to ERPs recorded from normal and dyslexic children during two reading tasks. We found that characteristic latency and amplitude changes of ERP components are due to task and pathology. Our results support the idea that dyslexia involves different and complex cerebral functions aside from the language system. This mathematical approach provides reproducible analysis criteria that are crucial for the reliability of ERP analysis.},
author = {Casarotto, S. and Bianchi, A.M. and Cerutti, S. and Chiarenza, G.A.},
doi = {10.1109/MEMB.2005.1384103},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Dynamic Time Warping.pdf:pdf},
issn = {0739-5175},
journal = {IEEE Engineering in Medicine and Biology Magazine},
mendeley-groups = {EEGWaveformAnalysis,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {1},
pages = {68--77},
pmid = {15709539},
title = {{Dynamic time warping in the analysis of event-related potentials}},
url = {http://ieeexplore.ieee.org/document/1384103/},
volume = {24},
year = {2005}
}
@article{Ouyang2011,
abstract = {Event-related brain potentials (ERPs) are important research tools because they provide insights into mental processing at high temporal resolution. Their usefulness, however, is limited by the need to average over a large number of trials, sacrificing information about the trial-by-trial variability of latencies or amplitudes of specific ERP components. Here we propose a novel method based on an iteration strategy of the residues of averaged ERPs (RIDE) to separate latency-variable component clusters. The separated component clusters can then serve as templates to estimate latencies in single trials with high precision. By applying RIDE to data from a face-priming experiment, we separate priming effects and show that they are robust against latency shifts and within-condition variability. RIDE is useful for a variety of data sets that show different degrees of variability and temporal overlap between ERP components.},
author = {Ouyang, Guang and Herzmann, Grit and Zhou, Changsong and Sommer, Werner},
doi = {10.1111/j.1469-8986.2011.01269.x},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/RIDE.pdf:pdf},
isbn = {1469-8986},
issn = {00485772},
journal = {Psychophysiology},
keywords = {Component separation,Event-related potentials,Face recognition,Mental chronometry,Single-trial responses},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {12},
pages = {1631--1647},
pmid = {21895682},
title = {{Residue iteration decomposition (RIDE): A new method to separate ERP components on the basis of latency variability in single trials}},
volume = {48},
year = {2011}
}
@article{Bandt2002,
abstract = {We introduce complexity parameters for time series based on comparison of neighboring values. The definition directly applies to arbitrary real-world data. For some well-known chaotic dynamical systems it is shown that our complexity behaves similar to Lyapunov exponents, and is particularly useful in the presence of dynamical or observational noise. The advantages of our method are its simplicity, extremely fast calculation, robustness, and invariance with respect to nonlinear monotonous transformations.},
author = {Bandt, Christoph and Pompe, Bernd},
doi = {10.1103/PhysRevLett.88.174102},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Permutation Entropy.pdf:pdf},
isbn = {0031-9007},
issn = {0031-9007},
journal = {Physical Review Letters},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {17},
pages = {1--17},
pmid = {12005759},
title = {{Permutation Entropy: A Natural Complexity Measure for Time Series}},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.88.174102},
volume = {88},
year = {2002}
}
@article{Jorn2011,
abstract = {We present a new paradigm for the adaptive estimation of evoked brain responses in single trials, based upon the combination of the matching pursuit (MP) algorithm and template matching, and referred to as Template Matching Pursuit (TMP). In contrast to the classical template matching with invariant single-trial morphology and to previous approaches using MP with strong similarity constraint on functions in sequential trials, this adaptive approach allows for a wide variety of waveforms, and its universality is retained by parametrizing all relevant waveforms in terms of Gabor functions. A survey of single-trial estimates obtained for 10 subjects (???4000 individual trials in total) confirms the validity of the assumption of a good approximation of single-trial waveforms. Owing to the fully parametric approach, we can easily perform also any quantitative analysis of such a huge dataset. As an example we take the trial-to-trial variability of the peak amplitude and latency of the auditory M100 component. This methodology provides estimates of diversified morphologies, which makes it free from the limitations inherent to any restrictive model. This seems advantageous in the context of the ongoing debate as to the neural mechanisms of average evoked brain responses. ?? 2011 Elsevier B.V.},
author = {J{\"{o}}rn, M. and Sieluzycki, C. and Matysiak, M. A. and Zygierewicz, J. and Scheich, H. and Durka, P. J. and K{\"{o}}nig, R.},
doi = {10.1016/j.jneumeth.2011.04.019},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Single trial reconstruction of auditory evoked fields using template matching pursuit.pdf:pdf},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {Auditory cortex,Evoked responses,Magnetoencephalography,Matching Pursuit,Single-trial analysis,Template matching},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {1},
pages = {119--128},
pmid = {21536070},
title = {{Single-trial reconstruction of auditory evoked magnetic fields by means of Template Matching Pursuit}},
volume = {199},
year = {2011}
}
@article{Rankine2007,
abstract = {This paper presents a new relative measure of signal complexity, referred to here as relative structural complexity (RSC), which is based on the matching pursuit (MP) decomposition. By relative, we refer to the fact that this new measure is highly dependent on the decomposition dictionary used by MP. The structural part of the definition points to the fact that this new measure is related to the structure, or composition, of the signal under analysis. After a formal definition, the proposed RSC measure is used in the analysis of newborn electroencephalogram (EEG). To do this, firstly, a time-frequency decomposition dictionary is specifically designed to compactly represent the newborn EEG seizure state using MP. We then show, through the analysis of synthetic and real newborn EEG data, that the relative structural complexity measure can indicate changes in EEG structure as it transitions between the two EEG states; namely seizure and background (non-seizure).},
author = {Rankine, L. and Mesbah, M. and Boashash, B.},
doi = {10.1007/s11517-006-0143-0},
file = {:Users/rramele/GoogleDrive/BCI/Shape BCI/Pattern Matching signal complexity measure for EEG.pdf:pdf},
issn = {01400118},
journal = {Medical and Biological Engineering and Computing},
keywords = {Coherent dictionary,Matching pursuit,Newborn EEG,Relative structural complexity,Time-frequency},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {3},
pages = {251--260},
pmid = {17221258},
title = {{A matching pursuit-based signal complexity measure for the analysis of newborn EEG}},
volume = {45},
year = {2007}
}
@inproceedings{Fornoni2014,
author = {Fornoni, Marco and Caputo, Barbara},
booktitle = {Proceedings - International Conference on Pattern Recognition},
doi = {10.1109/ICPR.2014.586},
isbn = {9781479952083},
issn = {10514651},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {aug},
pages = {3404--3409},
publisher = {IEEE},
title = {{Scene recognition with naive bayes non-linear learning}},
url = {http://ieeexplore.ieee.org/document/6977298/},
year = {2014}
}
@article{Krusienski2006,
abstract = {This study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.},
author = {Krusienski, Dean J. and Sellers, Eric W. and Cabestaing, Fran{\c{c}}ois and Bayoudh, Sabri and McFarland, Dennis J. and Vaughan, Theresa M. and Wolpaw, Jonathan R.},
doi = {10.1088/1741-2560/3/4/007},
isbn = {1741-2560 (Print)$\backslash$n1741-2552 (Linking)},
issn = {17412560},
journal = {Journal of Neural Engineering},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {dec},
number = {4},
pages = {299--305},
pmid = {17124334},
title = {{A comparison of classification techniques for the P300 Speller}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/17124334 http://stacks.iop.org/1741-2552/3/i=4/a=007?key=crossref.0d0b93e9c0efb39f5c99794dfd09967a},
volume = {3},
year = {2006}
}
@article{Hu2010,
abstract = {Brief radiant laser pulses can be used to activate cutaneous A?? and C nociceptors selectively and elicit a number of transient brain responses in the ongoing EEG (N1, N2 and P2 waves of laser-evoked brain potentials, LEPs). Despite its physiological and clinical relevance, the early-latency N1 wave of LEPs is often difficult to measure reliably, because of its small signal-to-noise ratio (SNR), thus producing unavoidable biases in the interpretation of the results. Here, we aimed to develop a method to enhance the SNR of the N1 wave and measure its peak latency and amplitude in both average and single-trial waveforms. We obtained four main findings. First, we suggest that the N1 wave can be better detected using a central-frontal montage (Cc-Fz), as compared to the recommended temporal-frontal montage (Tc-Fz). Second, we show that the N1 wave is optimally detected when the neural activities underlying the N2 wave, which interfere with the scalp expression of the N1 wave, are preliminary isolated and removed using independent component analysis (ICA). Third, we show that after these N2-related activities are removed, the SNR of the N1 wave can be further enhanced using a novel approach based on wavelet filtering. Fourth, we provide quantitative evidence that a multiple linear regression approach can be applied to these filtered waveforms to obtain an automatic, reliable and unbiased estimate of the peak latency and amplitude of the N1 wave, both in average and single-trial waveforms. ?? 2009 Elsevier Inc. All rights reserved.},
author = {Hu, L. and Mouraux, A. and Hu, Y. and Iannetti, G. D.},
doi = {10.1016/j.neuroimage.2009.12.010},
isbn = {1095-9572 (Electronic)$\backslash$r1053-8119 (Linking)},
issn = {10538119},
journal = {NeuroImage},
keywords = {Event-related potentials (ERPs),Independent component analysis (ICA),Laser-evoked potentials (LEPs),Multiple linear regression,N1 wave,Single-trial analysis,Wavelet filtering},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {1},
pages = {99--111},
pmid = {20004255},
publisher = {Elsevier Inc.},
title = {{A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (ERPs) in single trials}},
url = {http://dx.doi.org/10.1016/j.neuroimage.2009.12.010},
volume = {50},
year = {2010}
}
@article{Renard2010,
abstract = {Abstract This paper describes the OpenViBE software platform which enables researchers to design, test, and use brain–computer interfaces (BCIs). BCIs are communication systems that enable users to send commands to computers solely by means of brain activity. BCIs are gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). The key features of the platform are (1) high modularity, (2) embedded tools for visualization and feedback based on VR and 3D displays, (3) BCI design made available to non-programmers thanks to visual programming, and (4) various tools offered to the different types of users. The platform features are illustrated in this paper with two entertaining VR applications based on a BCI. In the first one, users can move a virtual ball by imagining hand movements, while in the second one, they can control a virtual spaceship using real or imagined foot movements. Online experiments with these applicatio...},
archivePrefix = {arXiv},
arxivId = {10.1162/pres.19.1.35.},
author = {Renard, Yann and Lotte, Fabien and Gibert, Guillaume and Congedo, Marco and Maby, Emmanuel and Delannoy, Vincent and Bertrand, Olivier and L{\'{e}}cuyer, Anatole},
doi = {10.1162/pres.19.1.35},
eprint = {pres.19.1.35.},
isbn = {1054-7460},
issn = {1054-7460},
journal = {Presence: Teleoperators and Virtual Environments},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {feb},
number = {1},
pages = {35--53},
pmid = {6797525},
primaryClass = {10.1162},
title = {{OpenViBE: An Open-Source Software Platform to Design, Test, and Use Brain–Computer Interfaces in Real and Virtual Environments}},
url = {http://www.mitpressjournals.org/doi/10.1162/pres.19.1.35},
volume = {19},
year = {2010}
}
@incollection{Lotte2013,
abstract = {Brain-Computer Interfaces (BCI) are communication systems conveying messages through brain activity only. This paper elaborates on the suitability of BCI for 3D Video Games (VG). Thus, we first review some recent BCI-based 3D VG. We then discuss the limitations of current BCI technology, those being mainly related to usability and performances. Finally, we report on some areas in which BCI could be useful for 3D VG despite their limitations. More precisely, BCI could be useful as an additional control channel, to send commands that cannot be intuitively sent with other devices. BCI could also be used for mental state monitoring either 1) during the game, in order to make adaptive and dynamic video games or 2) during the game creation in order to maximizes some measures of game quality that could be derived from a tester's mental state.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Lotte, F. and Faller, J. and Guger, C.},
booktitle = {Proceedings of the 6th International Conference on Foundations of Digital Games},
doi = {10.1007/978-3-642-29746-5_10},
eprint = {arXiv:1011.1669v3},
isbn = {978-3-642-29745-8},
issn = {978-3-642-29745-8},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {1--24},
pmid = {25246403},
publisher = {Springer, Berlin, Heidelberg},
title = {{Combining BCI with Virtual Reality: Towards New Applications and Improved BCI}},
url = {http://hal.inria.fr/inria-00591574},
year = {2013}
}
@article{Farwell1988,
abstract = {This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min. {\textcopyright} 1988.},
author = {Farwell, L. A. and Donchin, E.},
doi = {10.1016/0013-4694(88)90149-6},
isbn = {0013-4694 (Print)},
issn = {00134694},
journal = {Electroencephalography and Clinical Neurophysiology},
keywords = {EEG,Event-related potential,P300,Prosthesis,Self-help device},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {dec},
number = {6},
pages = {510--523},
pmid = {2461285},
title = {{Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/2461285},
volume = {70},
year = {1988}
}
@article{DeVos2014,
abstract = {International Journal of Psychophysiology, Corrected proof. doi:10.1016/j.ijpsycho.2013.10.008},
author = {{De Vos}, Maarten and Debener, Stefan},
doi = {10.1016/j.ijpsycho.2013.10.008},
isbn = {0167-8760},
issn = {01678760},
journal = {International Journal of Psychophysiology},
mendeley-groups = {Entropy,EEGWaveformAnalysis,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {1},
pages = {1--2},
pmid = {24144634},
title = {{Mobile eeg: Towards brain activity monitoring during natural action and cognition}},
volume = {91},
year = {2014}
}
@inproceedings{Yamaguchi2009,
abstract = {Electroencephalograph (EEG) recordings during right and left hand motor imagery can be used to move a cursor to a target on a computer screen (such system is called BCI). Recently, we have proposed the detection method of error potential in order to add the fail safe function to BCI system. In this paper, feature extraction method based on morphological multi-resolution analysis is introduced to extract features concerned with motor imagery and cognition simultaneously from the EEG signals. Morphological filter is composed of nonlinear operation between signal and structural function and this multi-resolution analysis can be constructed by repeating this filtering to signal while changing structural function. This method is a kind of discrete wavelet analysis with non-linear characteristics and is effective to extract specific shapes. The structural function which decides the filter characteristic is designed to obtain optimal separation based on mutual information algorithm or spectrum dividing algorithm.},
author = {Yamaguchi, Tomonari and Fujio, Mitsuhiko and Inoue, Katsuhiro and Pfurtscheller, Gert},
booktitle = {2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009},
doi = {10.1109/ICICIC.2009.161},
isbn = {9780769538730},
keywords = {BCI,EEG,Error potential,Mathematical morphology,Multiresolution analysis},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {dec},
pages = {1558--1561},
publisher = {IEEE},
title = {{Design method of morphological structural function for pattern recognition of EEG signals during motor imagery and cognition}},
url = {http://ieeexplore.ieee.org/document/5412503/},
year = {2009}
}
@article{Carlson2013,
author = {Carlson, Tom and {del R. Millan}, Jose},
doi = {10.1109/MRA.2012.2229936},
issn = {1070-9932},
journal = {IEEE Robotics {\&} Automation Magazine},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {mar},
number = {1},
pages = {65--73},
title = {{Brain-Controlled Wheelchairs: A Robotic Architecture}},
url = {http://ieeexplore.ieee.org/document/6476692/},
volume = {20},
year = {2013}
}
@article{Chen2014,
abstract = {In this paper, we study if an electroencephalogram (EEG) signal can be treated as a pseudo-random number generator (PRNG). We approach this problem by calculating the frequency of both healthy and epileptic EEG signals, and we find that all EEG signals obey the Gaussian distribution with different standard deviations. However, under some transformation, we can treat EEG signals as PRNGs. Our transformation takes the sum of the least significant five bits of the EEG sample amplitude and then outputs the parity of this sum to a binary (0 or 1) sequence. Our binary sequences have passed nearly all NIST pseudo-random number tests except a few failures. This indicates that the EEG signals can indeed become a PRNG provided that it undergoes a transformation like ours. ?? 2014 Elsevier B.V. All rights reserved.},
author = {Chen, Guangyi},
doi = {10.1016/j.cam.2014.02.028},
issn = {03770427},
journal = {Journal of Computational and Applied Mathematics},
keywords = {Electroencephalogram (EEG),Gaussian distribution,Pseudo-random number generator (PRNG),Uniform distribution},
mendeley-groups = {Entropy,Thesis,Thesis2},
pages = {1--4},
title = {{Are electroencephalogram (EEG) signals pseudo-random number generators?}},
volume = {268},
year = {2014}
}
@book{Schomer2010,
abstract = {"This edition has several new features, reflective of the changes that have occurred in our field over the last 5 years since the fifth edition. More and more, the field of digital recording has expanded; however, in order to understand some of the shortcomings and pitfalls of digital EEG, people need to still address the issues of basic analog recording principles. With an increased use of digital recording, laboratories have collected new and different "technical artifacts." We present here an attempt to start a database for such artifacts in a hopes that future editions will continue to expand upon this and offer a fairly complete library for beginning individuals interested in our field. As noted in the fifth edition, epilepsy monitoring units (EMU's) have continued to mushroom. Similar growth has occurred in the use of EEG monitoring in newborn, cardiac, trauma, and post-operative intensive care units. With the significant advances in wireless communication and easy access to the Internet, such recordings can also be viewed and transmitted locally virtually instantaneously and can allow for well-trained clinical neurophysiologists to see and opine about patients' conditions on a very time-relevant basis. Hopefully, as future generations may show, this ability will significantly influence our patients' outcomes. Similarly, the field of intraoperative clinical neurophysiology for spinal cord function, cranial nerve function, and cranial vascular therapies has continued to evolve along with the wireless and iInternet communications. This has allowed for close monitoring of neurologic function during critical periods of operations, again with a time course that allows for corrective actions to be taken on a meaningful time frame"-Provided by publisher.},
author = {Schomer, Donald L and Silva, Fernando Lopes Da},
booktitle = {Book},
publisher = {Oxford University Press},
isbn = {0781789427},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {1296},
title = {{Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields}},
volume = {1},
year = {2010}
}

@book{Hartman2005,
abstract = {Organized by wave features rather than pattern names, this atlas helps$\backslash$nguide the reader to an EEG interpretation even when the waveform$\backslash$nis unfamiliar. The first section takes the reader through the process$\backslash$nof characterizing EEG waves by their features. The second section$\backslash$norganizes EEG patterns by their features, so provides EEG waveform$\backslash$ndifferential diagnoses. The third section is organized alphabetically$\backslash$nby pattern name with each pattern described in a way that allows$\backslash$nthe reader to distinguish it from similarly appearing patterns. Examples$\backslash$nof the patterns also are provided.},
author = {Stern, J and Engel Jr, J},
booktitle = {Neurology},
publisher = {Wolters Kluwer},
doi = {10.1212/01.wnl.0000174180.41994.39},
isbn = {978-1-4511-0963-4},
issn = {0028-3878},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,EEGWaveformAnalysis,Thesis,Thesis2},
pages = {1--467},
title = {{Atlas of EEG Patterns}},
year = {2005}
}
@inproceedings{SchwarzAndreasandSteyrlDavidandMuller-Putz2016,
abstract = {Non-invasive brain-computer interfaces (BCI) aim to assist severely motor impaired persons in their daily life routine, however only a few BCIs have made it out of the laboratory. To foster further development, the Cybathlon, an international multi-discipline tournament, has been founded. One of the disciplines is the BCI-Race, where end users control avatars in a virtual race game by their thoughts. The game supports 4 different commands which accelerate the avatar and increase the chance to win. So far, no gold},
author = {{Schwarz, Andreas and Steyrl, David and M{\"{u}}ller-Putz}, Gernot R.},
booktitle = {2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016)},
isbn = {9781509018970},
mendeley-groups = {Entropy,Thesis,Thesis2},
number = {October},
publisher = {IEEE},
title = {{Brain-Computer Interface adaptation for an end user to compete in the Cybathlon}},
year = {2016}
}
@article{Riener2014,
abstract = {The Cybathlon is a championship for racing pilots with disabilities (i.e., parathletes) who are using advanced assistive devices including robotic technologies. The competitions are comprised by different disciplines that apply the most modern powered devices such as prostheses, wearable exoskeletons, wheelchairs, functional electrical stimulation as well as novel brain-computer interfaces. The main goal of the Cybathlon is to provide a platform for the development of novel assistive technologies that are useful for daily life. Through the organization of the Cybathlon we want to help removing barriers between the public, people with disabilities and science. The first Cybathlon will take place on October 8, 2016. Thereafter, the Cybathlon will be held periodically every 2 to 4 years.},
author = {Riener, Robert and Seward, Linda J.},
doi = {10.1109/SMC.2014.6974351},
isbn = {978-1-4799-3840-7},
issn = {1062922X},
journal = {2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
keywords = {Competition,Cybathlon 2016,Exoskeletons,Prosthetics,assistive devices,bionic Olympics,brain-computer interfaces,brain-computer-interfaces,electrical stimulation,exoskeletons,functional electrical stimulation,handicapped aids,medical robotics,prosthesis,prosthetics,robot-assisted parathletes,robotic technologies,wearable exoskeletons,wheelchairs},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {oct},
pages = {2792--2794},
publisher = {IEEE},
title = {{Cybathlon 2016}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6974351},
year = {2014}
}
@article{Huggins2016,
author = {Huggins, Jane E. and Alcaide-Aguirre, Ramses E. and Hill, Katya},
doi = {10.1080/2326263X.2016.1203629},
issn = {2326-263X},
journal = {Brain-Computer Interfaces},
keywords = {brain-computer interface},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {2},
pages = {112--120},
title = {{Effects of text generation on P300 brain-computer interface performance}},
url = {http://www.tandfonline.com/doi/full/10.1080/2326263X.2016.1203629},
volume = {3},
year = {2016}
}
@article{Tibon2015,
abstract = {The cognitive events examined in many event-related potentials (ERPs) studies do not occur in a neural vacuum, and separating the signals of interest from the brain's background electrical activity generally requires averaging multiple EEG segments of a condition of interest (Luck, 2005). In addition to within-subject averaging, the vast majority of ERP studies are based on across-subject grand average data, i.e., group waveforms representing the means of subjects' averaged waveforms, with statistical significance examined by comparing variance between conditions of interest with variance between participants. Using this approach may not always portray a valid picture. Consider, for example, the following experimental paradigm: in a study of episodic associative memory, participants encoded 120 pairs of stimuli (unrelated object picture pairs in the unimodal task, and unrelated environmental sound-object picture pairs in the crossmodal task). At test, cue pictures were presented to probe recall of the associated picture (in the unimodal task) or sound (in the crossmodal task). ERPs were time-locked to the onset of the cue, and sorted post-hoc into recall-success and recall-failure trials (for details of the procedures, see Tibon and Levy, 2014a). This experimental design poses several challenges to the conventional grand-averaging method. First, since the assignment of trials to experimental conditions is based on participants' responses, it is quite likely that the data will be unbalanced (that is, an unequal number of trials in each condition). Therefore, signal-to-noise ratio and variance can vary significantly between experimental conditions. Second, since experimental conditions are mutually dependent (i.e., a participant who had 100 recall-success responses can only have 20 recall-failure responses), participants who were very successful (having a low number of recall-failure trials) or very unsuccessful (having a low number of recall-success trials) in performing the task are likely to be excluded due to an insufficient number of trials for addressing signal-to-noise ratio (SNR) challenges in one of the experimental conditions. What},
author = {Tibon, Roni and Levy, Daniel A.},
doi = {10.3389/fpsyg.2015.00555},
issn = {16641078},
journal = {Frontiers in Psychology},
keywords = {EEG/ERP,Event-related potentials,Mixed-effects models,Repeated-measures ANOVA,Unbalanced data},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {5},
pages = {15},
pmid = {25983716},
publisher = {Frontiers Media SA},
title = {{Striking a balance: Analyzing unbalanced event-related potential data}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25983716 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4416363},
volume = {6},
year = {2015}
}
@inproceedings{Ramele2015,
abstract = {The aim of this paper is to present a case study report about the applicability of thermography and algometry by pressure in the evaluation of lactation which causes breast engorgement. The case study was conducted in the Human Milk Bank of the “Universidade Federal do Paran{\'{a}} Hospital” in Curitiba, Paran{\'{a}} - Brazil. We selected two women (who were breastfeeding), one without and one with breast engorgement. Then, we compare the different patterns of engorgement, temperature and tolerance to painful pressure before and after treatment, which consisted of massage and manual milking. Initial measurements indicated that normal puerperal breasts were cooler than ingurgitated breasts; also it was found that the normal breasts were more tolerant to pain pressure. After treatment, the engorged breasts have their temperature reduced and also represented more tolerance to pain. The technologies applied, although with some limitations, have provided preliminary important information, which combined with the clinical examination showed different patterns of breast engorgement, temperature and tolerance to pain, comparing the events before and after treatment.},
author = {Ramele, R. and Villar, A. J. and Santos, J. M.},
booktitle = {IFMBE Proceedings},
doi = {10.1007/978-3-319-13117-7_142},
isbn = {9783319131160},
issn = {16800737},
keywords = {BCI,Classification,Feature Extraction,Feature Matching,SIFT},
mendeley-groups = {Entropy,Thesis2},
pages = {556--559},
publisher = {Springer International Publishing},
title = {{A brain computer interface classification method based on sift descriptors}},
url = {http://link.springer.com/10.1007/978-3-319-13117-7{\_}142},
volume = {49},
year = {2015}
}
@article{Liang2008,
author = {Liang, Nanying and Bougrain, Laurent},
journal = {4th International Brain-Computer {\ldots}},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {1--6},
title = {{Averaging techniques for single-trial analysis of oddball event-related potentials}},
url = {http://hal.archives-ouvertes.fr/inria-00337070/},
year = {2008}
}
@article{Riccio2013,
abstract = {The purpose of this study was to investigate the support of attentional and memory processes in controlling a P300-based brain-computer interface (BCI) in people with amyotrophic lateral sclerosis (ALS). Eight people with ALS performed two behavioral tasks: (i) a rapid serial visual presentation (RSVP) task, screening the temporal filtering capacity and the speed of the update of the attentive filter, and (ii) a change detection task, screening the memory capacity and the spatial filtering capacity. The participants were also asked to perform a P300-based BCI spelling task. By using correlation and regression analyses, we found that only the temporal filtering capacity in the RSVP task was a predictor of both the P300-based BCI accuracy and of the amplitude of the P300 elicited performing the BCI task. We concluded that the ability to keep the attentional filter active during the selection of a target influences performance in BCI control.},
author = {Riccio, Angela and Simione, Luca and Schettini, Francesca and Pizzimenti, Alessia and Inghilleri, Maurizio and Belardinelli, Marta Olivetti and Mattia, Donatella and Cincotti, Febo},
doi = {10.3389/fnhum.2013.00732},
isbn = {1662-5161},
issn = {1662-5161},
journal = {Frontiers in Human Neuroscience},
keywords = {P300,amyotrophic lateral sclerosis,attention,brain computer interface,p300,working memory},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {11},
pages = {732},
pmid = {24282396},
title = {{Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3825256{\&}tool=pmcentrez{\&}rendertype=abstract},
volume = {7},
year = {2013}
}
@article{Guger2009a,
abstract = {An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8{\%} (N = 81) of the subjects were able to spell with 100{\%} accuracy in the RC paradigm and 55.3{\%} (N = 38) of the subjects spelled with 100{\%} accuracy in the SC paradigm. Less than 3{\%} of the subjects did not spell any character correctly. People who slept less than 8 h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89{\%} of the 81 RC subjects were able to spell with accuracy 80-100{\%}. A similar study using a motor imagery BCI with 99 subjects showed that only 19{\%} of the subjects were able to achieve accuracy of 80-100{\%}. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar. ?? 2009 Elsevier Ireland Ltd.},
author = {Guger, Christoph and Daban, Shahab and Sellers, Eric and Holzner, Clemens and Krausz, Gunther and Carabalona, Roberta and Gramatica, Furio and Edlinger, Guenter},
doi = {10.1016/j.neulet.2009.06.045},
isbn = {1872-7972 (Electronic)},
issn = {03043940},
journal = {Neuroscience Letters},
keywords = {Brain-computer interface (BCI),Electroencephalogram (EEG),P300 potential,Rehabilitation,Spelling device},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis2},
number = {1},
pages = {94--98},
pmid = {19545601},
title = {{How many people are able to control a P300-based brain-computer interface (BCI)?}},
volume = {462},
year = {2009}
}
@incollection{Liberczuk2015,
author = {Liberczuk, Sergio and Fr{\'{i}}as, Bruno Cernuschi},
booktitle = {IFMBE Proceedings},
doi = {10.1007/978-3-319-13117-7_132},
isbn = {9783319131160},
issn = {16800737},
keywords = {EEG Inverse Problem,HMM,Particle Filtering,Sequential Monte Carlo},
mendeley-groups = {Entropy,Thesis,Thesis2},
pages = {516--519},
publisher = {Springer International Publishing},
title = {{Particle filtering applied to the EEG inverse problem with dependent noises}},
url = {http://link.springer.com/10.1007/978-3-319-13117-7{\_}132},
volume = {49},
year = {2015}
}
@article{Jure2016,
abstract = {Nowadays, strokes are a growing cause of mortality and many people remain with motor sequelae and troubles in the daily activities. To treat this sequelae, alternative rehabilitation techniques are needed. In this article a Brain Computer Interface (BCI) system to control a Functional Electrical Stimulation (FES) system is presented. It can be used as a novel tool in easy setup clinical routines, to improve the rehabilitation process by mean of detecting patient´s motor intention, performing it by FES and finally receiving appropriate feedback The BCI-FES system presented here, consists of three blocks: the first one decodes the patient´s intention and it is composed by the patient, the acquisition hardware and the processing software (Emotiv EPOC{\textregistered}). The second block, based on Arduino's technology, transforms the information into a valid command signal. The last one excites the patient´s neuromuscular system by means of a FES device. In order to evaluate the cerebral activity sensed by the device, topographic maps were obtained. The BCI-FES system was able to detect the patient´s motor intention and control the FES device. At the time of this publication, the system it's being employing in a rehabilitation program with patients post stroke.},
author = {Jure, Fabricio A and Carrere, Luc{\'{i}}a C and Gentiletti, Gerardo G and Tabernig, Carolina B},
doi = {10.1088/1742-6596/705/1/012058},
issn = {1742-6588},
journal = {Journal of Physics: Conference Series},
keywords = {Arduino,BCI,Emotiv,FES,Neuro-Rehabilitation,Stroke},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
mendeley-tags = {Arduino,BCI,Emotiv,FES,Neuro-Rehabilitation,Stroke},
month = {apr},
number = {1},
pages = {012058},
publisher = {IOP Publishing},
title = {{BCI-FES system for neuro-rehabilitation of stroke patients}},
url = {http://stacks.iop.org/1742-6596/705/i=1/a=012058?key=crossref.e4e42c8e6ea5d9ff10fe5b597ef6c490},
volume = {705},
year = {2016}
}
@article{Schmidtmann2013,
abstract = {The proportion of signal elements embedded in noise needed to detect a signal is a standard tool for investigating motion perception. This paradigm was applied to the shape domain to determine how local information is pooled into a global percept. Stimulus arrays consisted of oriented Gabor elements that sampled the circumference of concentric radial frequency (RF) patterns. Individual Gabors were oriented tangentially to the shape (signal) or randomly (noise). In different conditions, signal elements were located randomly within the entire array or constrained to fall along one of the concentric contours. Coherence thresholds were measured for RF patterns with various frequencies (number of corners) and amplitudes ("sharpness" of corners). Coherence thresholds (about 10{\%} = 15 elements) were lowest for circular shapes. Manipulating shape frequency or amplitude showed a range where thresholds remain unaffected (frequency ≤ RF4; amplitude ≤ 0.05). Increasing either parameter caused thresholds to rise. Compared to circles, thresholds increased by approximately four times for RF13 and five times for amplitudes of 0.3. Confining the signals to individual contours significantly reduced the number of elements needed to reach threshold (between 4 and 6), independent of the total number of elements on the contour or contour shape. Finally, adding external noise to the orientation of the elements had a greater effect on detection thresholds than adding noise to their position. These results provide evidence for a series of highly sensitive, shape-specific analysers which sum information globally but only from within specific annuli. These global mechanisms are tuned to position and orientation of local elements from which they pool information. The overall performance for arrays of elements can be explained by the sensitivity of multiple, independent concentric shape detectors rather than a single detector integrating information widely across space (e.g. Glass pattern detector).},
author = {Loffler, G. and Bennett, D. M. and Gordon, G. E.},
doi = {10.1167/7.9.202},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Schmidtmann et al. - 2013 - Detecting shapes in noise tuning characteristics of global shape mechanisms.pdf:pdf},
isbn = {1662-5188 (Electronic)$\backslash$r1662-5188 (Linking)},
issn = {1534-7362},
journal = {Journal of Vision},
keywords = {contour,form,global shape,signal integration,texture},
mendeley-groups = {EEGWaveformAnalysis,Thesis,Thesis2},
number = {9},
pages = {202--202},
pmid = {23720625},
publisher = {Frontiers Media SA},
title = {{Seeing shape in noise: tuning characteristics of global shape mechanisms}},
url = {http://jov.arvojournals.org/Article.aspx?doi=10.1167/7.9.202},
volume = {7},
year = {2010}
}
@article{Knuth2006,
abstract = {Electric potentials and magnetic fields generated by ensembles of synchronously active neurons, either spontaneously or in response to external stimuli, provide information essential to understanding the processes underlying cognitive and sensorimotor activity. Interpreting recordings of these potentials and fields is difficult because detectors record signals simultaneously generated by various regions throughout the brain. We introduce a novel approach to this problem, the differentially variable component analysis (dVCA) algorithm, which relies on trial-to-trial variability in response amplitude and latency to identify multiple components. Using simulations we demonstrate the importance of response variability to component identification, the robustness of dVCA to noise, and its ability to characterize single-trial data. We then compare the source-separation capabilities of dVCA with those of principal component analysis and independent component analysis. Finally, we apply dVCA to neural ensemble activity recorded from an awake, behaving macaque-demonstrating that dVCA is an important tool for identifying and characterizing multiple components in the single trial.},
author = {Knuth, K. H.},
doi = {10.1152/jn.00663.2005},
isbn = {0022-3077 (Print)},
issn = {0022-3077},
journal = {Journal of Neurophysiology},
mendeley-groups = {Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {5},
pages = {3257--3276},
pmid = {16467431},
title = {{Differentially Variable Component Analysis: Identifying Multiple Evoked Components Using Trial-to-Trial Variability}},
url = {http://jn.physiology.org/cgi/doi/10.1152/jn.00663.2005},
volume = {95},
year = {2006}
}
@article{Patel2005,
abstract = {The Event-Related Potential (ERP) is a time-locked measure of electrical activity of the cerebral surface representing a distinct phase of cortical processing. Two components of the ERP which bear special importance to stimulus evaluation, selective attention, and conscious discrimination in humans are the P300 positivity and N200 negativity, appearing 300 ms and 200 ms post-stimulus, respectively. With the rapid proliferation of high-density EEG methods, and interdisciplinary interest in its application as a prognostic, diagnostic, and investigative tool, an understanding of the underpinnings of P300 and N200 physiology may support its application to both the basic neuroscience and clinical medical settings. The authors present a synthesis of current understanding of these two deflections in both normal and pathological states.},
author = {Patel, Salil H. and Azzam, Pierre N.},
doi = {10.7150/ijms.2.147},
isbn = {1449-1907 (Electronic)},
issn = {14491907},
journal = {International Journal of Medical Sciences},
keywords = {Electroencephalography (EEG),N2,Neuroimaging,P3,Selective attention},
mendeley-groups = {Entropy,EEGWaveformAnalysis,Thesis,Thesis2},
number = {4},
pages = {147--154},
pmid = {16239953},
title = {{Characterization of N200 and P300: Selected studies of the Event-Related Potential}},
url = {http://www.medsci.org/v02p0147.htm},
volume = {2},
year = {2005}
}
@article{Alvarado-Gonzalez2016,
abstract = {We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject's P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA's performance using our shape-feature vector was 93 {\%} , that is, 10 {\%} higher than the one obtained with BCI2000-feature's vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of 0.88 . Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8 . Finally, we found that the electrode C4 also leads to better classification.},
author = {Alvarado-Gonz{\'{a}}lez, Montserrat and Gardu{\~{n}}o, Edgar and Bribiesca, Ernesto and Y{\'{a}}{\~{n}}ez-Su{\'{a}}rez, Oscar and Medina-Ba{\~{n}}uelos, Ver{\'{o}}nica},
doi = {10.1155/2016/2029791},
issn = {1748-670X},
journal = {Computational and Mathematical Methods in Medicine},
mendeley-groups = {Entropy,EEGWaveformAnalysis,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {1--14},
publisher = {Hindawi Publishing Corporation},
title = {{P300 Detection Based on EEG Shape Features}},
url = {http://www.hindawi.com/journals/cmmm/2016/2029791/},
volume = {2016},
year = {2016}
}
@inproceedings{Ramele2016,
author = {Ramele, R. and Villar, A.J. and Santos, J.M.},
booktitle = {2016 4th International Winter Conference on Brain-Computer Interface (BCI)},
doi = {10.1109/IWW-BCI.2016.7457454},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Ramele, Villar, Santos - 2016 - BCI classification based on signal plots and SIFT descriptors.pdf:pdf},
isbn = {978-1-4673-7841-3},
keywords = {BCI classification,Brain modeling,Classification algorithms,Electroencephalography,Feature extraction,Medical services,Rhythm,SIFT descriptors,Visualization,bci,bnci,brain computer interfaces,brain-computer interfaces,brain/neural computer interaction field,classification,clinical adoption,electroencephalography,feature-extraction,human observer,medical signal processing,sift,signal plots,transforms,visually relevant feature descriptors},
mendeley-groups = {Thesis,Thesis2},
month = {feb},
pages = {1--4},
publisher = {IEEE},
title = {{BCI classification based on signal plots and SIFT descriptors}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7457454},
year = {2016}
}
@article{Goldberger2000,
abstract = {Abstract--The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet.org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.},
author = {Goldberger, A. L. and Amaral, L. A. N. and Glass, L. and Hausdorff, J. M. and Ivanov, P. Ch. and Mark, R. G. and Mietus, J. E. and Moody, G. B. and Peng, C.-K. and Stanley, H. E.},
doi = {10.1161/01.CIR.101.23.e215},
issn = {0009-7322},
journal = {Circulation},
mendeley-groups = {BCI2016,Thesis,Thesis2},
month = {jun},
number = {23},
pages = {e215--e220},
title = {{PhysioBank, PhysioToolkit, and PhysioNet : Components of a New Research Resource for Complex Physiologic Signals}},
url = {http://circ.ahajournals.org/content/101/23/e215.short},
volume = {101},
year = {2000}
}
@article{Bresenham1965,
abstract = {The algorithm can be programmed without the use of multiplication or division. It was found that 333 core locations were sufficient for an IBM 1401 program (used to control an IBM 1627). The average computation time between successive incrementations was approximately 1.5 milliseconds.},
author = {Bresenham, J. E.},
journal = {IBM Systems Journal},
mendeley-groups = {BCI2016,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {1},
pages = {25--30},
title = {{Algorithm for computer control of a digital plotter}},
volume = {4},
year = {1965}
}
@book{Allison2013,
address = {Berlin, Heidelberg},
doi = {10.1007/978-3-642-29746-5},
editor = {Allison, Brendan Z. and Dunne, Stephen and Leeb, Robert and {Del R. Mill{\'{a}}n}, Jos{\'{e}} and Nijholt, Anton},
isbn = {978-3-642-29745-8},
mendeley-groups = {BCI2016,Thesis2},
publisher = {Springer Berlin Heidelberg},
series = {Biological and Medical Physics, Biomedical Engineering},
title = {{Towards Practical Brain-Computer Interfaces}},
url = {http://link.springer.com/10.1007/978-3-642-29746-5},
year = {2013}
}
@article{Steyrl2015,
abstract = {There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate - for the first time - that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3{\%}. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.},
author = {Steyrl, David and Scherer, Reinhold and Faller, Josef and M{\"{u}}ller-Putz, Gernot R},
doi = {10.1515/bmt-2014-0117},
issn = {1862-278X},
journal = {Biomedizinische Technik. Biomedical engineering},
keywords = {1515,bmt-2014-0117,brain-computer interfaces,doi 10,machine learning,random forests,regularized linear discriminant analysis,sensorimotor rhythms},
mendeley-groups = {BCI2016,Thesis,Thesis2},
pmid = {25830903},
title = {{Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25830903},
pages = {77--86},
year = {2015}
}
@article{Ben-Simon2013,
abstract = {The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes-open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data-driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes-open/eyes-closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time-course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top-down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom-up processing of sensory input.},
annote = {It contains a good description of what alpha waves are, how to gather eeg data. It also says that they visually inspect the signals in order to remove artifacts.},
author = {Ben-Simon, Eti and Podlipsky, Ilana and Okon-Singer, Hadas and Gruberger, Michal and Cvetkovic, Dean and Intrator, Nathan and Hendler, Talma},
doi = {10.1111/ejn.12083},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Ben-Simon et al. - 2013 - The dark side of the alpha rhythm FMRI evidence for induced alpha modulation during complete darkness.pdf:pdf},
isbn = {1460-9568 (Electronic)$\backslash$r0953-816X (Linking)},
issn = {0953816X},
journal = {European Journal of Neuroscience},
keywords = {EEG classification,Inhibition hypothesis,Intrinsic alertness,Simultaneous EEG-fMRI,The idle rhythm},
mendeley-groups = {BCI2016,Thesis,Thesis2},
number = {October 2012},
pages = {795--803},
pmid = {23216771},
title = {{The dark side of the alpha rhythm: FMRI evidence for induced alpha modulation during complete darkness}},
volume = {37},
year = {2013}
}
@article{Ahn2013,
abstract = {In most brain computer interface (BCI) systems, some target users have significant difficulty in using BCI systems. Such target users are called 'BCI-illiterate'. This phenomenon has been poorly investigated, and a clear understanding of the BCI-illiteracy mechanism or a solution to this problem has not been reported to date. In this study, we sought to demonstrate the neurophysiological differences between two groups (literate, illiterate) with a total of 52 subjects. We investigated recordings under non-task related state (NTS) which is collected during subject is relaxed with eyes open. We found that high theta and low alpha waves were noticeable in the BCI-illiterate relative to the BCI-literate people. Furthermore, these high theta and low alpha wave patterns were preserved across different mental states, such as NTS, resting before motor imagery (MI), and MI states, even though the spatial distribution of both BCI-illiterate and BCI-literate groups did not differ. From these findings, an effective strategy for pre-screening subjects for BCI illiteracy has been determined, and a performance factor that reflects potential user performance has been proposed using a simple combination of band powers. Our proposed performance factor gave an r = 0.59 (r(2) = 0.34) in a correlation analysis with BCI performance and yielded as much as r = 0.70 (r(2) = 0.50) when seven outliers were rejected during the evaluation of whole data (N = 61), including BCI competition datasets (N = 9). These findings may be directly applicable to online BCI systems.},
author = {Ahn, Minkyu and Cho, Hohyun and Ahn, Sangtae and Jun, Sung Chan},
doi = {10.1371/journal.pone.0080886},
file = {:Users/rramele/Library/Application Support/Mendeley Desktop/Downloaded/Ahn et al. - 2013 - High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery.pdf:pdf},
issn = {1932-6203},
journal = {PloS one},
keywords = {Adult,Alpha Rhythm,Alpha Rhythm: physiology,Brain-Computer Interfaces,Discriminant Analysis,Educational Status,Female,Humans,Knowledge,Male,Rest,Rest: physiology,Theta Rhythm,Theta Rhythm: physiology},
mendeley-groups = {BCI2016,Thesis,Thesis2},
month = {jan},
number = {11},
pages = {1--11},
pmid = {24278339},
publisher = {Public Library of Science},
title = {{High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery}},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0080886},
volume = {8},
year = {2013}
}
@article{Boiman2008,
abstract = {State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric nearest-neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between these two families of approaches rendered NN-based image classifiers useless. We claim that the effectiveness of non-parametric NN-based image classification has been considerably undervalued. We argue that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: (i) Quantization of local image descriptors (used to generate "bags-of-words ", codebooks). (ii) Computation of 'image-to-image' distance, instead of 'image-to-class' distance. We propose a trivial NN-based classifier - NBNN, (Naive-Bayes nearest-neighbor), which employs NN- distances in the space of the local image descriptors (and not in the space of images). NBNN computes direct 'image- to-class' distances without descriptor quantization. We further show that under the Naive-Bayes assumption, the theoretically optimal image classifier can be accurately approximated by NBNN. Although NBNN is extremely simple, efficient, and requires no learning/training phase, its performance ranks among the top leading learning-based image classifiers. Empirical comparisons are shown on several challenging databases (Caltech-101 ,Caltech-256 and Graz-01).},
annote = {This paper is the NBNN method which is similar to what I am doing with Sift descriptors.},
author = {Boiman, Oren and Shechtman, Eli and Irani, Michal},
doi = {10.1109/CVPR.2008.4587598},
isbn = {9781424422432},
issn = {1063-6919},
journal = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
mendeley-groups = {SIFT.Papers,Entropy,BCI2016,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {i},
title = {{In defense of nearest-neighbor based image classification}},
pages={10--18},
year = {2008}
}
@article{Vedaldi2010,
abstract = {E-participation is a relatively new approach, so it is necessary to evaluate it carefully so that we can improve e-participation practice. This paper describes a framework that has been developed for evaluating a number of e-participation pilots in the legislation development processes of parliaments. The framework is based on the objectives and basic characteristics of 'traditional' public participation, e-participation and the legislation development processes, as well as the existing frameworks for the evaluation of Information Systems (ISs), e-participation and traditional public participation. It includes three perspectives: process, system and outcomes evaluation; each of them is analysed into a number of evaluation criteria.},
author = {Vedaldi, Andrea and Fulkerson, Brian},
doi = {10.1145/1873951.1874249},
isbn = {9781605589336},
journal = {Design},
keywords = {computer vision,image classification,object recognition,vi},
mendeley-groups = {SIFT.Papers,Entropy,BCI2016,Histogram of Oriented Gradients P300,Thesis,Thesis2},
number = {1},
pages = {1--4},
title = {{VLFeat - An open and portable library of computer vision algorithms}},
url = {http://vision.ucla.edu/{~}brian/papers/vedaldi10vlfeat.pdf},
volume = {3},
year = {2010}
}
@article{Lowe2004,
abstract = {SIFT: SCALE INVARIANT FEATURE TRANSFORM BY DAVID  Presented by: Jason Clemons Page 2. Overview  Sub Pixel Locate Potential Feature Points Build  Descriptors Assign  Orientations Filter Edge and Low Contrast Responses },
author = {Lowe, G},
journal = {International Journal},
mendeley-groups = {SIFT.Papers,Entropy,BCI2016,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {91--110},
title = {{SIFT - The Scale Invariant Feature Transform}},
volume = {2},
year = {2004}
}
@article{Diez2013,
abstract = {This work presents a brain-computer interface (BCI) used to operate a robotic wheelchair. The experiments were performed on 15 subjects (13 of them healthy). The BCI is based on steady-state visual-evoked potentials (SSVEP) and the stimuli flickering are performed at high frequency (37, 38, 39 and 40 Hz). This high frequency stimulation scheme can reduce or even eliminate visual fatigue, allowing the user to achieve a stable performance for long term BCI operation. The BCI system uses power-spectral density analysis associated to three bipolar electroencephalographic channels. As the results show, 2 subjects were reported as SSVEP-BCI illiterates (not able to use the BCI), and, consequently, 13 subjects (12 of them healthy) could navigate the wheelchair in a room with obstacles arranged in four distinct configurations. Volunteers expressed neither discomfort nor fatigue due to flickering stimulation. A transmission rate of up to 72.5 bits/min was obtained, with an average of 44.6 bits/min in four trials. These results show that people could effectively navigate a robotic wheelchair using a SSVEP-based BCI with high frequency flickering stimulation.},
author = {Diez, Pablo F and {Torres M{\"{u}}ller}, Sandra M and Mut, Vicente A and Laciar, Eric and Avila, Enrique and Bastos-Filho, Teodiano Freire and Sarcinelli-Filho, M{\'{a}}rio},
doi = {10.1016/j.medengphy.2012.12.005},
issn = {1873-4030},
journal = {Medical engineering {\&} physics},
keywords = {Adult,BCI INAUT Argentina,Biofeedback, Psychology,Biofeedback, Psychology: instrumentation,Brain-Computer Interfaces,Electroencephalography,Electroencephalography: instrumentation,Equipment Design,Equipment Failure Analysis,Evoked Potentials, Visual,Female,Humans,Male,Man-Machine Systems,Middle Aged,Paralysis,Paralysis: physiopathology,Paralysis: rehabilitation,Photic Stimulation,Photic Stimulation: instrumentation,Photic Stimulation: methods,Robotics,Robotics: instrumentation,Therapy, Computer-Assisted,Therapy, Computer-Assisted: instrumentation,Therapy, Computer-Assisted: methods,Visual Cortex,Visual Cortex: physiopathology,Visual Perception,Wheelchairs,Young Adult},
mendeley-groups = {BCI,Thesis,Thesis2},
mendeley-tags = {BCI INAUT Argentina},
month = {aug},
number = {8},
pages = {1155--64},
pmid = {23339894},
title = {{Commanding a robotic wheelchair with a high-frequency steady-state visual evoked potential based brain-computer interface.}},
url = {http://www.sciencedirect.com/science/article/pii/S1350453312003335},
volume = {35},
year = {2013}
}
@inproceedings{Liu2012,
abstract = {In recent years, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received much attentions. However, most SSVEP based BCI devices are not portable and have high price, which are not suitable to be used for clinical and commercial purpose. Thanks to the low cost and portable Emotiv EPOC, it brings BCI into daily life. In this paper, SSVEP based BCI through Emotiv EPOC is implemented. BCI 2000 is employed to connect Emotiv EPOC and Matlab to implement the online system. The online experiments have the accuracy of 95.83±3.59 {\%}, information transfer rate (ITR) with 22.85±1.85 bits/min and detection duration of 5.25±2.14 sec.},
author = {Liu, Yue and Jiang, Xiao and Cao, Teng and Wan, Feng and Mak, Peng Un and Mak, Pui-In and Vai, Mang I},
booktitle = {2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings},
doi = {10.1109/VECIMS.2012.6273184},
isbn = {978-1-4577-1759-8},
issn = {1944-9429},
keywords = {Accuracy,BCI 2000,Brain computer interfaces,Correlation,EEG signals,EPOC Emotiv,Electroencephalography,Emotiv EPOC,ITR,MATLAB,Matlab,Monitoring,SSVEP based BCI device,SSVEP signals,Visualization,brain-computer interface,brain-computer interfaces,brain-computer interfaces (BCI),canonical correlation cnalysis (CCA),electroencephalography,information transfer rate,mathematics computing,medical signal processing,steady-state visual evoked potential (SSVEP),steady-state visual evoked potential based brain-c},
mendeley-groups = {BCI/Emotiv,Thesis,Thesis2},
mendeley-tags = {EPOC Emotiv},
month = {jul},
pages = {34--37},
publisher = {IEEE},
shorttitle = {Virtual Environments Human-Computer Interfaces and},
title = {{Implementation of SSVEP based BCI with Emotiv EPOC}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6273184},
year = {2012}
}
@article{Debener2012,
abstract = {To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77{\%}) and outdoor (69{\%}) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.},
author = {Debener, Stefan and Minow, Falk and Emkes, Reiner and Gandras, Katharina and de Vos, Maarten},
doi = {10.1111/j.1469-8986.2012.01471.x},
issn = {1540-5958},
journal = {Psychophysiology},
keywords = {Adult,Brain,Brain: physiology,EPOC Emotiv,Electroencephalography,Electroencephalography: economics,Electroencephalography: instrumentation,Electroencephalography: methods,Equipment Design,Equipment Design: economics,Equipment Design: standards,Event-Related Potentials, P300,Event-Related Potentials, P300: physiology,Female,Humans,Male,Monitoring, Ambulatory,Monitoring, Ambulatory: instrumentation,Monitoring, Ambulatory: standards,Walking,Young Adult},
mendeley-groups = {BCI/Emotiv,Thesis,Thesis2},
mendeley-tags = {EPOC Emotiv},
month = {nov},
number = {11},
pages = {1617--21},
pmid = {23013047},
title = {{How about taking a low-cost, small, and wireless EEG for a walk?}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/23013047},
volume = {49},
year = {2012}
}
@inproceedings{Cheung2007,
abstract = {We present a fully automated multimodal medical image matching technique. Our method extends the concepts used in the computer vision SIFT technique for extracting and matching distinctive scale invariant features in 2D scalar images to scalar images of arbitrary dimensionality. This extension involves using hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. These features were successfully applied to determine accurate feature point correspondence between pairs of medical images (3D) and dynamic volumetric data (3D+time).},
author = {Cheung, Warren and Hamarneh, Ghassan},
booktitle = {2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
doi = {10.1109/ISBI.2007.356953},
isbn = {1-4244-0671-4},
keywords = {Bioinformatics,Biomedical imaging,Breast,Gaussian approximation,Histograms,Image analysis,Kernel,Laplace equations,Multidimensional systems,Object recognition,SIFT,bci sift,biomedical MRI,computerised tomography,dynamic volumetric data,feature extraction,hyperspherical coordinates,image matching,invariant feature transform,medical image matching,medical image processing,multidimensional histograms},
mendeley-groups = {SIFT.Papers,Thesis2},
mendeley-tags = {bci sift},
pages = {720--723},
publisher = {IEEE},
shorttitle = {Biomedical Imaging: From Nano to Macro, 2007. ISBI},
title = {{N-SIFT: N-DIMENSIONAL SCALE INVARIANT FEATURE TRANSFORM FOR MATCHING MEDICAL IMAGES}},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4193387},
year = {2007}
}
@book{Szeliski2010,
abstract = {Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http://szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision. Dr. Richard Szeliski has more than 25 years' experience in computer vision research, most notably at Digital Equipment Corporation and Microsoft Research. This text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford.},
author = {Szeliski, Richard},
isbn = {1848829345},
mendeley-groups = {SIFT.Papers,BCI/Visual Covert Attention,Thesis,Thesis2},
pages = {812},
publisher = {Springer},
title = {{Computer Vision: Algorithms and Applications}},
url = {http://books.google.com.ar/books/about/Computer{\_}Vision.html?id=8{\_}2RNQEACAAJ{\&}pgis=1},
volume = {24},
year = {2010}
}
@article{Stopczynski2014,
abstract = {Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system - Smartphone Brain Scanner - combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.},
annote = {Great scientific review of the gaming EEG devices. Good solid data.},
author = {Stopczynski, Arkadiusz and Stahlhut, Carsten and Larsen, Jakob Eg and Petersen, Michael Kai and Hansen, Lars Kai},
doi = {10.1371/journal.pone.0086733},
editor = {Marinazzo, Daniele},
issn = {1932-6203},
journal = {PloS one},
keywords = {Emotiv,Neurosky},
mendeley-groups = {BCI/Emotiv,Thesis,Thesis2},
mendeley-tags = {Emotiv,Neurosky},
month = {jan},
number = {2},
pages = {10},
pmid = {24505263},
publisher = {Public Library of Science},
title = {{The smartphone brain scanner: a portable real-time neuroimaging system.}},
url = {http://dx.plos.org/10.1371/journal.pone.0086733},
volume = {9},
year = {2014}
}
@book{WolpawJonathanR2012,
abstract = {In the last 15 years, a recognizable surge in the field of Brain Computer Interface (BCI) research and development has emerged. This emergence has sprung from a variety of factors. For one, inexpensive computer hardware and software is now available and can support the complex high-speed analyses of brain activity that is essential is BCI. Another factor is the greater understanding of the central nervous system including the abundance of new information on the nature and functional correlates of brain signals and improved methods for recording these signals in both the short-term and long-term. And the third, and perhaps most significant factor, is the new recognition of the needs and abilities of people disabled by disorders such as cerebral palsy, spinal cord injury, stroke, amyotrophic lateral sclerosis (ALS), multiple sclerosis, and muscular dystrophies. The severely disabled are now able to live for many years and even those with severely limited voluntary muscle control can now be given the most basic means of communication and control because of the recent advances in the technology, research, and applications of BCI. This book is intended to provide an introduction to and summary of essentially all major aspects of BCI research and development. Its goal is to be a comprehensive, balanced, and coordinated presentation of the field's key principles, current practice, and future prospects.},
author = {{Wolpaw, Jonathan R}, Wolpaw Elizabeth},
isbn = {0195388852},
mendeley-groups = {BCI,Neurobooks,Entropy,BCI2016,Histogram of Oriented Gradients P300,Thesis,Thesis2},
pages = {400},
publisher = {Oxford University Press},
title = {{Brain-Computer Interfaces: Principles and Practice}},
url = {http://books.google.com/books?hl=en{\&}lr={\&}id=0dST2Lg4KVYC{\&}pgis=1},
year = {2012}
}
@book{van2006signal,
author = {{Van Drongelen}, Wim},
mendeley-groups = {Neurobooks,Entropy,Histogram of Oriented Gradients P300,Thesis,Thesis2},
publisher = {Academic press},
title = {{Signal processing for neuroscientists: an introduction to the analysis of physiological signals}},
year = {2006}
}
@book{Clark2008,
abstract = {When historian Charles Weiner found pages of Nobel Prize-winning physicist Richard Feynman's notes, he saw it as a "record" of Feynman's work. Feynman himself, however, insisted that the notes were not a record but the work itself. In Supersizing the Mind, Andy Clark argues that our thinking doesn't happen only in our heads but that "certain forms of human cognizing include inextricable tangles of feedback, feed-forward and feed-around loops: loops that promiscuously criss-cross the boundaries of brain, body and world." The pen and paper of Feynman's thought are just such feedback loops, physical machinery that shape the flow of thought and enlarge the boundaries of mind. Drawing upon recent work in psychology, linguistics, neuroscience, artificial intelligence, robotics, human-computer systems, and beyond, Supersizing the Mind offers both a tour of the emerging cognitive landscape and a sustained argument in favor of a conception of mind that is extended rather than "brain-bound." The importance of this new perspective is profound. If our minds themselves can include aspects of our social and physical environments, then the kinds of social and physical environments we create can reconfigure our minds and our capacity for thought and reason.},
author = {Clark, Andy},
booktitle = {Consciousness and Cognition},
doi = {10.1093/acprof},
isbn = {0195333217},
issn = {02604027},
mendeley-groups = {Embodiment,Thesis,Thesis2},
number = {7},
pages = {286},
publisher = {Oxford University Press},
series = {Philosophy of mind; Philosophy of mind series},
title = {{Supersizing the Mind: Embodiment, Action, and Cognitive Extension}},
url = {http://www.amazon.com/dp/0195333217},
volume = {66},
year = {2008}
}
@article{Galan2008,
abstract = {To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair.},
author = {Gal{\'{a}}n, F and Nuttin, M and Lew, E and Ferrez, P W and Vanacker, G and Philips, J and Mill{\'{a}}n, J Del R},
doi = {10.1016/j.clinph.2008.06.001},
issn = {1388-2457},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
keywords = {Brain,Brain Mapping,Brain: physiology,Electroencephalography,Electroencephalography: methods,Humans,Robotics,User-Computer Interface,Wheelchairs},
mendeley-groups = {Assitive Robotics,Thesis,Thesis2},
month = {sep},
number = {9},
pages = {2159--69},
pmid = {18621580},
title = {{A brain-actuated wheelchair: asynchronous and non-invasive Brain-computer interfaces for continuous control of robots.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/18621580},
volume = {119},
year = {2008}
}
@article{Schalk2004,
abstract = {Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.},
author = {Schalk, G. and McFarland, D. J. and Hinterberger, T. and Birbaumer, N. and Wolpaw, J. R.},
doi = {10.1109/TBME.2004.827072},
issn = {0018-9294},
journal = {IEEE Transactions on Bio-medical Engineering},
keywords = {Algorithms,Brain,Brain: physiology,Cognition,Communication Aids for Disabled,Computer Peripherals,Electroencephalography,Electroencephalography: instrumentation,Electroencephalography: methods,Equipment Design,Equipment Failure Analysis,Equipment Failure Analysis: methods,Evoked Potentials,Evoked Potentials: physiology,Humans,Systems Integration,User-Computer Interface},
mendeley-groups = {BCI,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {jun},
number = {6},
pages = {1034--43},
pmid = {15188875},
title = {{BCI2000: a general-purpose brain-computer interface (BCI) system.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15188875},
volume = {51},
year = {2004}
}
@article{Wolpaw2002,
abstract = {For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or 'locked in', with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10-25bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.},
author = {Wolpaw, Jonathan R and Birbaumer, Niels and McFarland, Dennis J and Pfurtscheller, Gert and Vaughan, Theresa M},
issn = {1388-2457},
journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
keywords = {Brain Diseases,Brain Diseases: rehabilitation,Communication Aids for Disabled,Computer Systems,Electroencephalography,Electroencephalography: instrumentation,Humans,User-Computer Interface},
mendeley-groups = {BCI,Histogram of Oriented Gradients P300,Thesis,Thesis2},
month = {jun},
number = {6},
pages = {767--91},
pmid = {12048038},
title = {{Brain-computer interfaces for communication and control.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/12048038},
volume = {113},
year = {2002}
}