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README.rst
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.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17699192.svg
:target: https://doi.org/10.5281/zenodo.17699192
:alt: DOI
.. image:: https://github.com/braindecode/braindecode/workflows/docs/badge.svg
:target: https://github.com/braindecode/braindecode/actions/workflows/docs.yml
:alt: Docs Build Status
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:target: https://github.com/braindecode/braindecode/actions/workflows/tests.yml
:alt: Test Build Status
.. image:: https://codecov.io/gh/braindecode/braindecode/branch/master/graph/badge.svg
:target: https://codecov.io/gh/braindecode/braindecode
:alt: Code Coverage
.. image:: https://img.shields.io/pypi/v/braindecode?color=blue&style=flat-square
:target: https://pypi.org/project/braindecode/
:alt: PyPI
.. image:: https://img.shields.io/pypi/v/braindecode?label=version&color=orange&style=flat-square
:target: https://pypi.org/project/braindecode/
:alt: Version
.. image:: https://img.shields.io/pypi/pyversions/braindecode?style=flat-square
:target: https://pypi.org/project/braindecode/
:alt: Python versions
.. image:: https://pepy.tech/badge/braindecode
:target: https://pepy.tech/project/braindecode
:alt: Downloads
.. |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
########
Citing
########
If you use Braindecode in scientific work, please cite the software using the global
Zenodo DOI shown in the badge below:
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.17699192.svg
:target: https://doi.org/10.5281/zenodo.17699192
:alt: DOI
You can use the following BibTeX entry:
.. code-block:: bibtex
@software{braindecode,
author = {Aristimunha, Bruno and
Guetschel, Pierre and
Wimpff, Martin and
Gemein, Lukas and
Rommel, Cedric and
Banville, Hubert and
Sliwowski, Maciej and
Wilson, Daniel and
Brandt, Simon and
Gnassounou, Théo and
Paillard, Joseph and
{Junqueira Lopes}, Bruna and
Sedlar, Sara and
Moreau, Thomas and
Chevallier, Sylvain and
Gramfort, Alexandre and
Schirrmeister, Robin Tibor},
title = {Braindecode: toolbox for decoding raw electrophysiological brain data
with deep learning models},
url = {https://github.com/braindecode/braindecode},
doi = {10.5281/zenodo.17699192},
publisher = {Zenodo},
license = {BSD-3-Clause},
}
Additionally, we highly encourage you to cite the article that originally introduced the
Braindecode library and has served as a foundational reference for many works on deep
learning with EEG recordings. Please use the following reference:
.. 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.