Braindecode

A deep learning toolbox to decode raw time-domain EEG.

For EEG researchers who want to work with deep learning and deep learning researchers who want to work with EEG data. For now focused on convolutional networks.

Installation

1. Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
2. Install MOABB from https://github.com/NeuroTechX/moabb, needed if you want to use MOABB datasets utilities:

pip install download
python -c "from download import download; download('https://raw.githubusercontent.com/NeuroTechX/moabb/master/requirements.txt', 'requirements.txt', replace=True)"
pip install -r requirements.txt
rm requirements.txt
pip install -U https://github.com/NeuroTechX/moabb/archive/master.zip

3. Install latest release of braindecode via pip:

pip install braindecode

alternatively, if you use conda, you could create a dedicated environment with the following:

curl -O https://raw.githubusercontent.com/braindecode/braindecode/master/environment.yml
conda env create -f environment.yml
conda activate braindecode

alternatively, install the latest version of braindecode via pip:

pip install -U https://api.github.com/repos/braindecode/braindecode/zipball/master

Documentation

Documentation is online under https://braindecode.org

Dataset

The high-gamma dataset used in our publication (see below), including trained models, is available under: https://web.gin.g-node.org/robintibor/high-gamma-dataset/

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},
}