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.
- Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
- If you want to download EEG datasets from MOABB, install it:
pip install moabb
- 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 is online under https://braindecode.org, both in the stable and dev versions.
Guidelines for contributing to the library can be found on the braindecode github:
https://github.com/braindecode/braindecode/blob/master/CONTRIBUTING.md
If you use Braindecode in scientific work, please cite the software using the global Zenodo DOI shown in the badge below:
You can use the following BibTeX entry:
@software{braindecode, author = {Aristimunha, Bruno and Guetschel, Pierre and Wimpff, Martin and Gemein, Lukas and Rommel, Cedric and Banville, Hubert and Sliwowski, Maciej and Wilson, Daniel and Brandt, Simon and Gnassounou, Théo and Paillard, Joseph and {Junqueira Lopes}, Bruna and Sedlar, Sara and Moreau, Thomas and Chevallier, Sylvain and Gramfort, Alexandre and Schirrmeister, Robin Tibor}, title = {Braindecode: toolbox for decoding raw electrophysiological brain data with deep learning models}, url = {https://github.com/braindecode/braindecode}, doi = {10.5281/zenodo.17699192}, publisher = {Zenodo}, license = {BSD-3-Clause}, }
Additionally, we highly encourage you to cite the article that originally introduced the Braindecode library and has served as a foundational reference for many works on deep learning with EEG recordings. Please use the following reference:
@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}, }
This project is primarily licensed under the BSD-3-Clause License.
Some components within this repository are licensed under other licenses, including Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), MIT and Apache-2.0.
Please refer to the LICENSE and NOTICE files for the per-file list and more
detailed information.