Neuro-GPT: Towards A Foundation Model for EEG

• URL: http://arxiv.org/abs/2311.03764v4
• Date: Sat, 2 Mar 2024 07:06:39 GMT
• Title: Neuro-GPT: Towards A Foundation Model for EEG
• Authors: Wenhui Cui, Woojae Jeong, Philipp Th\"olke, Takfarinas Medani, Karim Jerbi, Anand A. Joshi, Richard M. Leahy
• Abstract summary: We propose Neuro-GPT, a foundation model consisting of an EEG encoder and a GPT model. Foundation model is pre-trained on a large-scale data set using a self-supervised task that learns how to reconstruct masked EEG segments. Experiments demonstrate that applying a foundation model can significantly improve classification performance compared to a model trained from scratch.
• Score: 0.04188114563181615
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, 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, which provides evidence for the generalizability of the foundation model and its ability to address challenges of data scarcity and heterogeneity in EEG. The code is publicly available at github.com/wenhui0206/NeuroGPT.

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