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:alt: Join the chat at https://gitter.im/braindecodechat/community
:target: https://gitter.im/braindecodechat/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
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:target: https://circleci.com/gh/braindecode/braindecode
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:target: https://codecov.io/gh/braindecode/braindecode
:alt: Code Coverage
.. |Braindecode| image:: https://user-images.githubusercontent.com/42702466/177958779-b00628aa-9155-4c51-96d1-d8c345aff575.svg
.. _braindecode: braindecode.org/
Braindecode
===========
Braindecode is an open-source Python toolbox for decoding raw electrophysiological brain
data with deep learning models. It includes dataset fetchers, data preprocessing and
visualization tools, as well as implementations of several deep learning
architectures and data augmentations for analysis of EEG, ECoG and MEG.
For neuroscientists who want to work with deep learning and
deep learning researchers who want to work with neurophysiological data.
Installation Braindecode
========================
1. Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
2. If you want to download EEG datasets from `MOABB <https://github.com/NeuroTechX/moabb>`_, install it:
.. code-block:: bash
pip install moabb
3. Install latest release of braindecode via pip:
.. code-block:: bash
pip install braindecode
If you want to install the latest development version of braindecode, please refer to `contributing page <https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md>`__
Documentation
=============
Documentation is online under https://braindecode.org, both in the stable and dev versions.
Contributing to Braindecode
===========================
Guidelines for contributing to the library can be found on the braindecode github:
https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md
Braindecode chat
================
https://gitter.im/braindecodechat/community
Citing
======
If you use this code in a scientific publication, please cite us as:
.. code-block:: bibtex
@article {HBM:HBM23730,
author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
journal = {Human Brain Mapping},
issn = {1097-0193},
url = {http://dx.doi.org/10.1002/hbm.23730},
doi = {10.1002/hbm.23730},
month = {aug},
year = {2017},
keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
brain–computer interface, model interpretability, brain mapping},
}
as well as the `MNE-Python <https://mne.tools>`_ software that is used by braindecode:
.. code-block:: bibtex
@article{10.3389/fnins.2013.00267,
author={Gramfort, Alexandre and Luessi, Martin and Larson, Eric and Engemann, Denis and Strohmeier, Daniel and Brodbeck, Christian and Goj, Roman and Jas, Mainak and Brooks, Teon and Parkkonen, Lauri and Hämäläinen, Matti},
title={{MEG and EEG data analysis with MNE-Python}},
journal={Frontiers in Neuroscience},
volume={7},
pages={267},
year={2013},
url={https://www.frontiersin.org/article/10.3389/fnins.2013.00267},
doi={10.3389/fnins.2013.00267},
issn={1662-453X},
}
Licensing
^^^^^^^^^
This project is primarily licensed under the BSD-3-Clause License.
Additional Components
~~~~~~~~~~~~~~~~~~~~~
Some components within this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International
License.
Please refer to the ``LICENSE`` and ``NOTICE`` files for more detailed
information.
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