Tools for bimodal training of CNNs, i.e. concurrent training with two data types
Project description
BiModNeuroCNN
This is a package for training bimodal deep learning archtectures on dual streams of neurological data. Package tested on Electroencephalography (EEG) and function near-infrared stpectroscopy (fNIRS).
Work in progress - more to be added in future.
Installation
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Install PyTorch: http://pytorch.org/
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Install Braindecode: https://github.com/braindecode/braindecode
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Install latest release of BiModNeuroCNN using pip:
pip install bimodneurocnn
Dataset
Link to dataset to be added upon upcoming publication.
Citing
Paper currently under review.
Braindecode was used to implement this package:
@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}, }
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