Metadata-Version: 2.1
Name: BiModNeuroCNN
Version: 0.1.0
Summary: Tools for bimodal training of CNNs, i.e. concurrent training with two data types
Home-page: https://github.com/cfcooney
Author: Ciaran Cooney
License: MIT License
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
Requires-Dist: braindecode (==0.4.85)
Requires-Dist: mne
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Requires-Dist: matplotlib

**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, brainâ€“machine interface,
>  brainâ€“computer interface, model interpretability, brain mapping},
>}





