Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training

Installation

We recommend installing the required packages using Python's native virtual environment. For Python 3.4+, this can be done as follows:

$ python -m venv ensemble_venv
$ source ensemble_venv/bin/activate
(ensemble_venv) $ pip install --upgrade pip
(ensemble_venv) $ pip install -r requirements.txt

Dataset preprocessing

Before training a model, you need to preprocess a dataset and then save its preprocessed version. To do this, use preprocess_dataset.py, running the following command:

(ensemble_venv) $ python preprocess_dataset.py

This process will create the folder preprocessed_data under the root directory.

Training

To train a model, you need to use run_experiment.py, running the following command:

(ensemble_venv) $ python run_experiment.py

The training process will create the following directories:

json        (note: contains the arguments of each experiment, stored in JSON format)
checkpoints (note: contains the saved model checkpoints, stored as .pth files)
results     (note: contains the experimental results as an .xlsx file and the train/val/test splits as a .pkl file)
plots_topo  (note: contains the visualizations of the model's spatial filters, saved as .eps and .svg files)

Acknowledgement

The research of Georgios Zoumpourlis was supported by QMUL Principal's Studentship.

Citation

[1] Georgios Zoumpourlis and Ioannis Patras. Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training. 12th IEEE International Winter Conference on Brain-Computer Interface (BCI), 2024.

Credits

Special thanks go to the authors of MOABB, Braindecode, MNE-Python and pyRiemann libraries, which have been essential for this project.

The current GitHub repo contains code parts from the following repository (heavily chopped, adapting and keeping some logging tools):
Code in third_party/skorch: https://github.com/skorch-dev/skorch
Credits go to its owners/developers. Its license is included in the corresponding folder.