gzoumpourlis / Ensemble-MI

PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"

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.