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softwares.html
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<!DOCTYPE HTML>
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<html>
<head>
<title>Softwares</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
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<body class="is-preload">
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<header id="header" class="alt style2">
<a href="index.html" class="logo"><strong>Etienne Combrisson</strong> <span>Softwares</span></a>
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<a href="#menu">Menu</a>
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<ul class="links">
<li><a href="index.html">Home</a></li>
<li><a href="research.html">Research</a></li>
<li><a href="publications.html">Publications</a></li>
<li><a href="bio.html">Bio</a></li>
<li><a href="softwares.html">Softwares</a></li>
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<header class="major">
<h2>Open-source Python softwares</h2>
</header>
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<img src="images/frites.png" alt="" width="25%" height="25%" data-position="center center" />
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<header class="major">
<h3>Frites</h3>
</header>
<p>
<b>Frites</b> (<i>Framework for Information Theoretical Analysis of Electrophysiological data and Statistics</i>) is a pure Python software to analyse neurophysiological data (M/EEG; ECoG; iEEG) by means of information theoretical measures (<a href="publications.html#combrisson2022frites">Combrisson et al. 2022, <i>JOSS</i></a>). It allows the estimation of functional connectivity and also includes relatively sophisticated group-level statistics (<a href="publications.html#combrisson2022group">Combrisson et al. 2022, <i>NeuroImage</i></a>). Frites is now included inside <a href="https://www.ebrains.eu/tools/frites-framework-for-information-theoretical-analysis-of-electrophysiological-data-and-statistics" target="_blank">EBRAINS</a>, "an open research infrastructure that gathers data, tools and computing facilities for brain-related research, built with interoperability at the core".
</p>
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<li><a href="https://github.com/brainets/frites" class="button" target="_blank"><i class="icon brands fa-github" aria-hidden="true"></i> Github</a></li>
<li><a href="https://brainets.github.io/frites/" class="button" target="_blank"><i class="fa fa-file" aria-hidden="true"></i> Doc</a></li>
</ul>
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<img src="images/visbrain_logo.png" alt="" width="25%" height="25%" data-position="center center" />
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<div class="inner">
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<h3>HOI</h3>
</header>
<p>
<b>HOI</b> (<i>Higher-Order Interactions</i>) is a Python software to estimate higher-order interactions (i.e. statistical dependencies between group of 3, 4...N variables). It relies on <a href="https://jax.readthedocs.io/en/latest/index.html" target="_blank">Jax</a>, a linear-algebra library to execute code on CPU, GPU and TPU. I mentored <a href="https://dishie2498.github.io/gsoc/" target="_blank">Dishie Vinchhi</a> during the 2023 <a href="https://summerofcode.withgoogle.com/programs/2023/projects/z6hGpvLS" target="_blank">Google Summer of Code</a>.
</p>
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<li><a href="https://github.com/brainets/hoi" class="button" target="_blank"><i class="icon brands fa-github" aria-hidden="true"></i> Github</a></li>
<li><a href="https://brainets.github.io/hoi/" class="button" target="_blank"><i class="fa fa-file" aria-hidden="true"></i> Doc</a></li>
</ul>
<hr>
</div>
</div>
</section>
<section>
<a href="generic.html" class="image">
<img src="images/tensorpac.png" alt="" width="20%" height="100%" data-position="top center" />
</a>
<div class="content">
<div class="inner">
<header class="major">
<h3>Tensorpac</h3>
</header>
<p>
<b>Tensorpac</b> is an open-source Python toolbox for computing Phase-Amplitude Coupling (PAC) using tensors and parallel computing (<a href="publications.html#combrisson2020tensorpac">Combrisson et al. 2020, <i>PLoS Comp. Biol.</i></a>). This software provides a modular implementation which allows one to combine existing methods for measuring PAC and chance distribution.
</p>
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<li><a href="https://github.com/EtienneCmb/tensorpac" class="button" target="_blank"><i class="icon brands fa-github" aria-hidden="true"></i> Github</a></li>
<li><a href="https://etiennecmb.github.io/tensorpac/" class="button" target="_blank"><i class="fa fa-file" aria-hidden="true"></i> Doc</a></li>
</ul>
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<section>
<img src="images/visbrain_logo.png" alt="" width="25%" height="25%" data-position="center center" />
<div class="content">
<div class="inner">
<header class="major">
<h3>Visbrain</h3>
</header>
<p>
<b>Visbrain</b> is a Python solution that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data (<a href="publications.html#combrisson2019visbrain">Combrisson et al. 2019, <i>Frontiers in Neuroinfo.</i></a>; <a href="publications.html#combrisson2017sleep">Combrisson et al. 2017, <i>Frontiers in Neuroinfo.</i></a>). Visbrain contains several modules to represent data on a 3D mesh (like intracranial implantation or projection) or polysomnographic data visualization and sleep analysis.
</p>
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<li><a href="https://github.com/EtienneCmb/visbrain" class="button" target="_blank"><i class="icon brands fa-github" aria-hidden="true"></i> Github</a></li>
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