A Convolutional Neural Network model paradigm for learning to classify M/EEG implemented with Keras and MNE. Including techniques to produce spatial and temporal visualizations of model insights.
First introduced in Machine learning for MEG during speech tasks, if you find any of the models, or tools herein useful, we kindly request that you cite this work.
This repository contains the layer and models proposed in the paper. As well as an example workflow using the popular and easy-to-access BCI competition IV dataset 2a motor imagery dataset.
- python 3
- Keras
- mne
- tqdm
- numpy
- scipy
- wget
This repository can be installed through pip using the following:
pip install git+https://github.com/SPOClab-ca/SCNN.git
Once installed, start by downloading the BCI IV 2a dataset using the get_bciiv2a module.
python -m SCNN.get_bciiv2a
Next we can train the base SCNN model for a subject of choice (there are 9). Starting with subject 1:
python -m SCNN.train_example 1
The best model will be saved by default to the file best_model.h5.
We can then use this model to train some activation visualizations
using:
python -m SCNN.activations best_model.h5
All the scripts have arguments that can configure their behaviour, try
running them with --help for more details