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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 provide important insights into interneuronal communication [@Canolty2010;@Silchenko2010;@Sherman2016]. Studies of these features in neural 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 the analysis of phase-amplitude coupling [@Zandvoort2021], non-sinusoidal waveshape [@Bartz2019], and time delays [@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 electrophysiology data. Code written in MATLAB exists for some 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 does exist in the free-to-use Python language - e.g., @Bachetti2024 - however these implementations are not tailored to use with electrophysiology data. The `PyBispectra` package addressed these problems by providing a single, comprehensive toolbox for bispectral analysis of electrophysiology data (\autoref{fig:overview}), including tutorials to facilitate an understanding of these analyses in the context of neuroscience research.
+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 electrophysiology data. Code written in MATLAB exists for some 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 does exist in the free-to-use Python language - e.g., @Bachetti2024 - however these implementations are not tailored to use with electrophysiology data. The `PyBispectra` package addresses these problems by providing a single, comprehensive toolbox for bispectral analysis of electrophysiology data (\autoref{fig:overview}), including tutorials to facilitate an understanding of these analyses in the context of neuroscience research.
 
 ![\label{fig:overview}Overview of the `PyBispectra` toolbox. Optional preprocessing methods are supported for the multivariate analysis of waveshape. Functions are provided for computing spectral representations of timeseries data. Classes are provided for computing cross-frequency coupling, time delays, and non-sinusoidal waveshape, with schematic visualisations of results shown. Also shown is an example code snippet for analysing phase-amplitude coupling.](Overview.svg)