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, install it:

pip install moabb

3. Install latest release of braindecode via pip:

pip install braindecode

If you want to install the latest development version of braindecode, please refer to contributing page

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:

@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 software that is used by braindecode:

@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.