Update refs

Author: tsbinns

paper/paper.bib

@@ -1,3 +1,15 @@
+@article{Bachetti2024,
+  title={Stingray 2: A fast and modern {P}ython library for spectral timing},
+  author={Matteo Bachetti and Daniela Huppenkothen and Abigail Stevens and John Swinbank and Guglielmo Mastroserio and Matteo Lucchini and Eleonora Veronica Lai and Johannes Buchner and Amogh Desai and Gaurav Joshi and Francesco Pisanu and Sri Guru Datta Pisupati and Swapnil Sharma and Mihir Tripathi and Dhruv Vats},
+  journal={Journal of Open Source Software},
+  volume={9},
+  number={102},
+  pages={7389},
+  year={2024},
+  publisher={The Open Journal},
+  doi={10.21105/joss.07389}
+}
+
 @article{Bartz2019,
   title={Analyzing the waveshape of brain oscillations with bicoherence},
   author={Bartz, Sarah and Avarvand, Forooz Shahbazi and Leicht, Gregor and Nolte, Guido},
@@ -243,25 +255,6 @@ @article{Silchenko2010
   doi={10.1016/j.jneumeth.2010.06.004}
 }
 
-@software{Stingray,
-  title={Stingray},
-  author={Matteo Bachetti and Daniela Huppenkothen and Usman Khan and Himanshu Mishra and Swapnil Sharma and Abbie Stevens and John Swinbank and Amogh Desai and Haroon Rashid and Evandro Martinez Ribeiro and Mihir Tripathi and Gaurav Joshi and Brigitta Sipőcz and pupper emeritus and Guglielmo Mastroserio and Dhruv Vats and tappina and omargamal8 and Meg Davis and Achilles Rasquinha and Paul Balm and Stuart Mumford and Devansh Shukla and \_youteakay and parkma99 and Riccardo Campana and Abhinav Kumar and Nitish Garg and Akash Tandon and Anurag Hota},
-  publisher={Zenodo},
-  doi={10.5281/zenodo.1490116}
-}
-
-@article{Bachetti2024,
-  title={Stingray 2: A fast and modern {P}ython library for spectral timing},
-  author={Matteo Bachetti and Daniela Huppenkothen and Abigail Stevens and John Swinbank and Guglielmo Mastroserio and Matteo Lucchini and Eleonora Veronica Lai and Johannes Buchner and Amogh Desai and Gaurav Joshi and Francesco Pisanu and Sri Guru Datta Pisupati and Swapnil Sharma and Mihir Tripathi and Dhruv Vats},
-  journal={Journal of Open Source Software},
-  volume={9},
-  number={102},
-  pages={7389},
-  year={2024},
-  publisher={The Open Journal},
-  doi={10.21105/joss.07389}
-}
-
 @article{Tort2010,
   title={Measuring phase-amplitude coupling between neuronal oscillations of different frequencies},
   author={Tort, Adriano BL and Komorowski, Robert and Eichenbaum, Howard and Kopell, Nancy},

paper/paper.md

@@ -49,7 +49,7 @@ Various forms of information can be extracted from neural timeseries data. Of th
 
 Analysis of phase-amplitude coupling, time delays, and non-sinusoidal waveshape characteristics provide important mechanistic insights into interneuronal communication [@Canolty2010;@Silchenko2010;@Sherman2016]. Studies of these features in neural timeseries data have been used to investigate core nervous system functions such as movement and memory, including their perturbation in disease [@deHemptinne2013;@Cole2017;@Bazzigaluppi2018;@Binns2024]. However, traditional methods for analysing this information have critical limitations that hinder their utility. In contrast, the bispectrum - the Fourier transform of the third order moment [@Nikias1987] - can be used for phase-amplitude coupling [@Zandvoort2021], non-sinusoidal waveshape [@Bartz2019], and time delay analyses [@Nikias1988], overcoming many of the limitations associated with traditional methods.
 
-Despite these benefits, the bispectrum has seen relatively little use in the field of neuroscience, in part due to the lack of an accessible, easy-to-use toolbox tailored to the analysis of electrophysiology data. Code written in MATLAB exists for electrophysiological analyses (see e.g., [github.com/sccn/roiconnect](https://github.com/sccn/roiconnect), [github.com/ZuseDre1/AnalyzingWaveshapeWithBicoherence](https://github.com/ZuseDre1/AnalyzingWaveshapeWithBicoherence)), however it is spread across multiple repositories, and often not in the form of a toolbox. Furthermore, use of this code requires a paid MATLAB license, limiting its accessibility. Code for computing the bispectrum can also be found written in the free-to-use Python language (e.g., @Stingray), however these implementations are not tailored to the analysis of electrophysiology data, limiting their use for neuroscience research. The `PyBispectra` package aims to address these limitations by providing a single, comprehensive toolbox for analysing phase-amplitude coupling, time delays, and non-sinusoidal waveshape characteristics in electrophysiology data with the bispectrum \autoref{fig:overview}, including tutorials to facilitate an understanding of these analyses in the context of neuroscience research. Data formats follow conventions from popular electrophysiological signal processing packages like `MNE-Python` [@Gramfort2013], and helper functions are provided as wrappers around `MNE-Python` and `SciPy` [@Virtanen2020] tools to facilitate data processing prior to bispectral analyses. Additional plotting tools are provided to visualise and aid the interpretation of results.
+Despite these benefits, the bispectrum has seen relatively little use in the field of neuroscience, in part due to the lack of an accessible, easy-to-use toolbox tailored to the analysis of electrophysiology data. Code written in MATLAB exists for electrophysiological analyses (see e.g., [github.com/sccn/roiconnect](https://github.com/sccn/roiconnect), [github.com/ZuseDre1/AnalyzingWaveshapeWithBicoherence](https://github.com/ZuseDre1/AnalyzingWaveshapeWithBicoherence)), however it is spread across multiple repositories, and often not in the form of a toolbox. Furthermore, use of this code requires a paid MATLAB license, limiting its accessibility. Code for computing the bispectrum can also be found written in the free-to-use Python language - e.g., @Bachetti2024 - however these implementations are not tailored to the analysis of electrophysiology data, limiting their use for neuroscience research. The `PyBispectra` package aims to address these limitations by providing a single, comprehensive toolbox for analysing phase-amplitude coupling, time delays, and non-sinusoidal waveshape characteristics in electrophysiology data with the bispectrum \autoref{fig:overview}, including tutorials to facilitate an understanding of these analyses in the context of neuroscience research. Data formats follow conventions from popular electrophysiological signal processing packages like `MNE-Python` [@Gramfort2013], and helper functions are provided as wrappers around `MNE-Python` and `SciPy` [@Virtanen2020] tools to facilitate data processing prior to bispectral analyses. Additional plotting tools are provided to visualise and aid the interpretation of results.
 
 ![Overview of the `PyBispectra` toolbox.\label{fig:overview}](Overview.png)