A deep learning toolbox to decode raw time-domain EEG.
For EEG researchers that want to want to work with deep learning and deep learning researchers that want to work with EEG data. For now focussed on convolutional networks.
- Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
- Install numpy (necessary for resamply installation to work), e.g.:
pip install numpy- Install braindecode via pip:
pip install braindecodeDocumentation is online under https://tntlfreiburg.github.io/braindecode/
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/
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},
}