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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

  1. Install PyTorch: http://pytorch.org/

  2. Install Braindecode: https://github.com/braindecode/braindecode

  3. 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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