Attention

This version is outdated, please use the new braindecode at https://braindecode.org/

Installation [outdated, see above]

  1. Install pytorch from http://pytorch.org/ (you don’t need to install torchvision).

  2. Install numpy (necessary for resamply installation to work), e.g.:

pip install numpy

  1. Install braindecode via pip:

pip install https://github.com/TNTLFreiburg/braindecode/archive/master.zip

Troubleshooting

Please report any issues on github: https://github.com/TNTLFreiburg/braindecode

API

braindecode.datautil

Utilities for data manipulation.

braindecode.experiments

Convenience classes for experiments, including monitoring and stop criteria.

braindecode.mne_ext

Extensions for the MNE library.

braindecode.models

Some predefined network architectures for EEG decoding.

braindecode.torch_ext

Torch extensions, for example new functions or modules.

braindecode.visualization

Functions for visualisations, especially of the ConvNets.

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},
}

Indices and tables