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 ============ 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.: .. code-block:: bash pip install numpy 3. Install braindecode via pip: .. code-block:: bash pip install braindecode Tutorials ========= .. toctree:: :maxdepth: 1 notebooks/Trialwise_Decoding.ipynb notebooks/Cropped_Decoding.ipynb notebooks/Trialwise_Manual_Training_Loop.ipynb notebooks/Cropped_Manual_Training_Loop.ipynb notebooks/visualization/Perturbation.ipynb Troubleshooting =============== Please report any issues on github: https://github.com/robintibor/braindecode API === .. autosummary:: :toctree: source braindecode.datautil braindecode.experiments braindecode.mne_ext braindecode.models braindecode.torch_ext braindecode.visualization Citing ====== If you use this code in a scientific publication, please cite us as: .. code-block:: bibtex @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 ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. _GitHub: https://github.com/robintibor/braindecode