Welcome to Braindecode¶
A deep learning toolbox to decode raw time-domain EEG.
For EEG researchers that want to work with deep learning and deep learning researchers that want to work with EEG data. For now focussed on convolutional networks.
Installation¶
- 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 braindecode
Tutorials¶
Troubleshooting¶
Please report any issues on github: https://github.com/robintibor/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},
}