We propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. The foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. We then fine-tune the model on a Motor Imagery Classification task to validate its performance in a low-data regime (9 subjects). Our experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch.

git clone [email protected]:wenhui0206/NeuroGPT.git
pip install -r requirements.txt
cd NeuroGPT/scripts
./train.sh

pip install -r requirements.txt

• TUH EEG Corpus
• BCI Competition IV 2a Dataset

This project is developed based on the following open-source repositories:

• Self-supervised learning of brain dynamics from broad neuroimaging data
• EEG-Conformer