Awesome EMG Data

Welcome to awesome-emg-data, a curated list of Electromyography (EMG) datasets and scholarly publications designed for researchers, practitioners, and enthusiasts in the field of biomedical engineering, neurology, kinesiology, and related disciplines. Our goal is to facilitate the discovery and accessibility of high-quality EMG data and cutting-edge research findings to foster innovation, education, and collaboration in the analysis, interpretation, and application of EMG signals.

What is EMG?

Electromyography (EMG) is a diagnostic procedure to assess the health of muscles and the nerve cells that control them (motor neurons). EMG results can reveal nerve dysfunction, muscle dysfunction or problems with nerve-to-muscle signal transmission. In research and application, EMG data is emerging as an important signal in developing prosthetics, enhancing sports performance, understanding muscular disorders, and in the design of interactive technologies.

Repository Contents

• Datasets: A comprehensive list of open-access EMG datasets, each dataset is accompanied by a link to the data source and a reference to the associated publication, if available.

How to Use This Repository

Browse through the lists to find datasets and research articles that suit your interests or project needs. The repository is structured to help you quickly identify relevant resources through categorization and tagging based on application domains, data types, and research topics.

Contributing

Contributions to awesome-emg-data are welcome! If you have a dataset, publication, or any other resource that you believe would be a valuable addition to our collection, please create an Issue or Pull Request to include the information. We appreciate your input in making this resource more comprehensive and useful for everyone.

Acknowledgments

We extend our gratitude to all researchers, contributors, and organizations who make their data and findings accessible to the public, thereby supporting the advancement of science and technology.

Datasets

• Dataset Paper Lobo-Prat, J. and Janssen, M. M. and Koopman, B. F. and Stienen, A. H. and De Groot, I. J. - Surface EMG signals in very late-stage of Duchenne muscular dystrophy: a case study, Journal of Neuroengineering and Rehabilitation, (2017).
• Dataset Paper Santuz, A. and Ekizos, A. and Kunimasa, Y. and Kijima, K. and Ishikawa, M. and Arampatzis, A. - Lower complexity of motor primitives ensures robust control of high-speed human locomotion, Heliyon, (2020).
• Dataset Paper Dwivedi, S. K. and Ngeo, J. and Shibata, T. - Extraction of nonlinear synergies for proportional and simultaneous estimation of finger kinematics, IEEE Transactions on Biomedical Engineering, (2020).
• Dataset Paper Jarque-Bou, N. - A large calibrated database of hand movements and grasps kinematics, Scientific Data, (2020).
• Dataset Paper Zhang, Y. and Li, P. and Zhu, X. and Su, S. W. and Guo, Q. and Xu, P. and Yao, D. - Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition, PloS one, (2017).
• Dataset Paper Ozdemir, M. A. and Kisa, D. H. and Guren, O. and Akan, A. - Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures, Data in Brief, (2022).
• Dataset Paper Mileti, I. and Serra, A. and Wolf, N. and Munoz-Martel, V. and Ekizos, A. and Palermo, E. and Arampatzis, A. and Santuz, A. - Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion, Frontiers in Bioengineering and Biotechnology, (2020).
• Dataset Paper Al-Yacoub, A. and Buerkle, A. and Flanagan, M. and Ferreira, P. and Hubbard, E. and Lohse, N. - Effective human-robot collaboration through wearable sensors, IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), (2020).
• Dataset Paper Kenville, R. and Maudrich, T. - Spectral properties of physiological mirror activity: an investigation of frequency features and common input between homologous muscles, Scientific Reports, (2022).
• Dataset (Ninapro DB1) Paper Atzori, M. et al. - Electromyography data for non-invasive naturally-controlled robotic hand prostheses, Scientific Data, (2014).
• Dataset (Ninapro DB2) Paper Atzori, M. et al. - Electromyography data for non-invasive naturally-controlled robotic hand prostheses, Scientific Fata, (2014).
• Dataset (Ninapro DB3) Paper Atzori, M. et al. - Electromyography data for non-invasive naturally-controlled robotic hand prostheses, Scientific Data, (2014).
• Dataset (Ninapro DB4) Paper Pizzolato, S. et al. - Comparison of six electromyography acquisition setups on hand movement classification tasks, PloS one, (2017).
• Dataset (Ninapro DB5) Paper Pizzolato, S. et al. - Comparison of six electromyography acquisition setups on hand movement classification tasks, PloS one, (2017).
• Dataset (Ninapro DB6) Paper Palermo, F. et al. - Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data, Journal Not Found, (2017).
• Dataset (Ninapro DB7) Paper Krasoulis, A. and Kyranou, I. and Erden, M. S. and Nazarpour, K. and Vijayakumar, S. - Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements, Journal of neuroengineering and rehabilitation, (2017).
• Dataset (Ninapro DB8) Paper Krasoulis, A. and Vijayakumar, S. and Nazarpour, K. - Effect of user practice on prosthetic finger control with an intuitive myoelectric decoder, Frontiers in neuroscience, (2019).
• Dataset (Ninapro DB9) Paper Jarque-Bou, N. - A large calibrated database of hand movements and grasps kinematics, Scientific Data, (2020). - Ninapro DB9 (this DB1 + DB2 + DB5 combined)
• Dataset (MeganePro 1 (MDS1) aka Ninapro DB10) Paper Cognolato, M. and Gijsberts, A. and Gregori, V. and Saetta, G. and Giacomino, K. and Hager, A. M. and Gigli, A. and Faccio, D. and Tiengo, C. and Bassetto, F. and others - Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics, Scientific Data, (2020).
• Dataset (MeganePro 2 (MDS2) aka Ninapro DB10 (likely to be same subjects as MDS4)) Paper Saetta, G. et al. - Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls, Scientific Data, (2020).
• Dataset (MeganePro 4 (MDS4) aka Ninapro DB10 (likely to be same subjects as MDS2)) Paper Saetta, G. et al. - Gaze, behavioral, and clinical data for phantom limbs after hand amputation from 15 amputees and 29 controls, Scientific Data, (2020).
• Dataset Paper Ellen, J. G. and Dash, M. B. - An artificial neural network for automated behavioral state classification in rats, PeerJ, (2021).
• Dataset Paper Benalca - An interactive system to improve cognitive abilities using electromyographic signals, ICAAI, (2021). - EMG-EPN-612.
• Dataset Paper Santuz, A. and Janshen, L. and Bru - Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults, Plos one, (2022).
• Dataset Paper Santuz, A. et al. - Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults, Plos one, (2022). - EMG-EPN-120.
• Dataset Paper Santuz, A. et al. - Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults, Plos one, (2022). - EMG-IMU-EPN-100+
• Dataset Paper Hug, F. - Individuals have unique muscle activation signatures as revealed during gait and pedaling, Journal of Applied Physiology, (2019).
• Dataset Paper Mohr, M. and von Tscharner, V. and Emery, C. A. and Nigg, B. M. - Classification of gait muscle activation patterns according to knee injury history using a support vector machine approach, Human Movement Science, (2019).
• Dataset Paper Pradhan, A. and He, J. and Jiang, N. - Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics, Scientific Data, (2022).
• Dataset Paper Zhao, K. and Wen, H. and Zhang, Z. and He, C. and Wu, J. - Fractal characteristics-based motor dyskinesia assessment, Biomedical Signal Processing and Control, (2021).
• Dataset Paper Donati, E. and Payvand, M. and Risi, N. and Krause, R. and Burelo, K. and Indiveri, G. and Dalgaty, T. and Vianello, E. - Processing EMG signals using reservoir computing on an event-based neuromorphic system, IEEE Biomedical Circuits and Systems Conference (BioCAS), (2018).
• Dataset Paper Wayne, P. M. and Manor, B. and Novak, V. and Costa, M. D. and Hausdorff, J. M. and Goldberger, A. L. and Ahn, A. C. and Yeh, G. Y. and Peng, C. and Lough, M. and others - A systems biology approach to studying Tai Chi, physiological complexity and healthy aging: design and rationale of a pragmatic randomized controlled trial, Contemporary Clinical Trials, (2013).
• Dataset Paper Radel, R. - Extending the limits of force endurance: stimulation of the motor or the frontal cortex?, Cortex, (2017).
• Dataset Paper Avrillon, S. and Del Vecchio, A. and Farina, D. and Pons, J. - Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task, Journal of Applied Physiology, (2021).
• Dataset Paper Jarque-Bou, N. - Identification of forearm skin zones with similar muscle activation patterns during activities of daily living, Journal of Neuroengineering and Rehabilitation, (2018).
• Dataset Paper Hamard, R. - A comparison of neural control of the biarticular gastrocnemius muscles between knee flexion and ankle plantar flexion, Journal of Applied Physiology, (2023).
• Dataset Paper Hug, F. - Muscles from the same muscle group do not necessarily share common drive: evidence from the human triceps surae, Journal of Applied Physiology, (2021).
• Dataset Paper Kurkin, S. and Badarin, A. and Grubov, V. and Maksimenko, V. and Hramov, A. - The oxygen saturation in the primary motor cortex during a single hand movement: functional near-infrared spectroscopy (fnirs) study, The European Physical Journal Plus, (2021).
• Dataset Paper MacLean, M. K. and Ferris, D. P. - Design and validation of a low-cost bodyweight support system for overground walking, Journal of Medical Devices, (2020).
• Dataset Paper Mohebian, M. R. et al. - Non-invasive decoding of the motoneurons: a guided source separation method based on convolution kernel compensation with clustered initial points, Frontiers in Computational Neuroscience, (2019).
• Dataset 2 Dataset 2 Paper Hybart, R. and Villancio-Wolter, K. S. and Ferris, D. P. - Metabolic cost of walking with electromechanical ankle exoskeletons under proportional myoelectric control on a treadmill and outdoors, PeerJ, (2023).
• Dataset Paper Zhang, W. and Yang, Z. and Li, H. and Huang, D. and Wang, L. and Wei, Y. and Zhang, L. and Ma, L. and Feng, H. and Pan, J. and others - Multimodal data for the detection of freezing of gait in Parkinson’s disease, Scientific Data, (2022).
• Dataset 1 Dataset 2 Paper Wang, H. and Basu, A. and Durandau, G. and Sartori, M. - A wearable real-time kinetic measurement sensor setup for human locomotion, Wearable Technologies, (2023).
• Dataset Paper Weinman, J. and Arfa-Fatollahkhani, P. and Zonnino, A. and Nikonowicz, R. C. and Sergi, F. - Effects of perturbation velocity, direction, background muscle activation, and task instruction on long-latency responses measured from forearm muscles, Frontiers in Human Neuroscience, (2021).
• Dataset Paper Nizamis, K. and Rijken, N. H. and Van Middelaar, R. and Neto, J. - Characterization of forearm muscle activation in duchenne muscular dystrophy via high-density electromyography: A case study on the implications for myoelectric control, Frontiers in Neurology, (2020).
• Dataset Paper Phillips, D. A. and Del Vecchio, A. R. and Carroll, K. and Matthews, E. L. - Developing a Practical Application of the Isometric Squat and Surface Electromyography, Biomechanics, (2021).
• Dataset Paper Mirakhorlo, M. and Maas, H. and Veeger, D. H. - Timing and extent of finger force enslaving during a dynamic force task cannot be explained by EMG activity patterns, Plos one, (2017).
• Dataset Paper Dimitrov, H. and Bull, A. M. and Farina, D. - High-density EMG, IMU, kinetic, and kinematic open-source data for comprehensive locomotion activities, Scientific Data, (2023).
• Dataset Paper Miljkovic et al. - Effect of the sEMG electrode (re) placement and feature set size on the hand movement recognition, Biomedical Signal Processing and Control, (2021).
• Dataset Paper Khan, A. M. and Khawaja, S. G. and Akram, M. U. and Khan, A. S. - sEMG dataset of routine activities, Data in Brief, (2020).
• Dataset Paper Hug, F. - Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators, Journal of Electromyography and Kinesiology, (2021).
• Dataset Paper Tokuda, K. and Nishikawa, M. and Kawahara, S. - Hippocampal state-dependent behavioral reflex to an identical sensory input in rats, PLoS One, (2014).
• Dataset Paper Wen, Y. and Kim, S. J. and Avrillon, S. and Levine, J. T. and Hug, F. - Toward a generalizable deep CNN for neural drive estimation across muscles and participants, Journal of Neural Engineering, (2023).
• Dataset Paper Xi, X. and Ma, C. and Yuan, C. and Miran, S. M. and Hua, X. and Zhao, Y. and Luo, Z. - Enhanced EEG--EMG coherence analysis based on hand movements, Biomedical Signal Processing and Control, (2020).
• Dataset Paper Albarracin et al. - Muscle function alterations in a Parkinson's disease animal model: Electromyographic recordings dataset, Data in Brief, (2022).
• Dataset Paper Gazzari, M. and Mattmann, A. and Maass, M. and Hollick, M. - My (o) Armband Leaks Passwords: An EMG and IMU Based Keylogging Side-Channel Attack, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, (2021).
• Dataset Paper Hamard, R. - Does different activation between the medial and the lateral gastrocnemius during walking translate into different fascicle behavior?, Journal of Experimental Biology, (2021).
• Dataset Paper Kang, J. W. and Kim, K. and Park, J. W. and Lee, S. J. - Classification of deep vein thrombosis stages using convolutional neural network of electromyogram with vibrotactile stimulation toward developing an early diagnostic tool: A preliminary study on a pig model, Plos one, (2023).
• Dataset Paper Koehn, R. R. and Roelker, S. A. and Pan, X. and Schmitt, L. C. and Chaudhari, A. M. and Siston, R. A. - Is modular control related to functional outcomes in individuals with knee osteoarthritis and following total knee arthroplasty?, Plos one, (2022).
• Dataset Paper Ceolini, E. and Frenkel, C. and Shrestha, S. B. and Taverni, G. and Khacef, L. and Payvand, M. and Donati, E. - Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing, Frontiers in Neuroscience, (2020).
• Dataset 1 Dataset 2 Paper Westermann, J. et al. - Measuring facial mimicry: Affdex vs. EMG, Plos one, (2024).
• Dataset Paper MacLean, M. K. and Ferris, D. P. - Design and validation of a low-cost bodyweight support system for overground walking, Journal of Medical Devices, (2020).
• Dataset Paper Hahne, J. M. and Graimann, B. and Muller, K. - Spatial filtering for robust myoelectric control, IEEE Transactions on Biomedical Engineering, (2012).
• Dataset Paper Khushaba, R. N. and Al-Timemy, A. and Kodagoda, S. and Nazarpour, K. - Combined influence of forearm orientation and muscular contraction on EMG pattern recognition, Expert Systems with Applications, (2016).
• Dataset Paper Han, M. et al. - HANDS: a multimodal dataset for modeling toward human grasp intent inference in prosthetic hands, Intelligent Service Robotics, (2020).
• Dataset Paper Teruya, P. Y. et al. - Quantifying muscle alterations in a Parkinson’s disease animal model using electromyographic biomarkers, Medical & Biological Engineering & Computing, (2021).