EEG Foundation Models for BCI Learn Diverse Features of Electrophysiology

TL;DR

This paper introduces a transformer-based self-supervised pre-training method for EEG data that enhances neural decoding and reveals diverse electrophysiological features, supporting various BCI tasks and understanding individual brain variability.

Contribution

It presents a novel EEG foundation model pre-training approach inspired by HuBERT, focusing on low-profile, real-time applications with minimal preprocessing and limited channels.

Findings

Supports standard BCI tasks like P300 and motor imagery

Learns features related to individual variability and alpha rhythms

Reveals diverse electrophysiological components in EEG data

Abstract

Brain computer interface (BCI) research, as well as increasing portions of the field of neuroscience, have found success deploying large-scale artificial intelligence (AI) pre-training methods in conjunction with vast public repositories of data. This approach of pre-training foundation models using label-free, self-supervised objectives offers the potential to learn robust representations of neurophysiology, potentially addressing longstanding challenges in neural decoding. However, to date, much of this work has focused explicitly on standard BCI benchmarks and tasks, which likely overlooks the multitude of features these powerful methods might learn about brain function as well as other electrophysiological information. We introduce a new method for self-supervised BCI foundation model pre-training for EEG inspired by a transformer-based approach adapted from the HuBERT framework originally developed for speech processing. Our pipeline is specifically focused on low-profile, real-time usage, involving minimally pre-processed data and just eight EEG channels on the scalp. We show that our foundation model learned a representation of EEG that supports standard BCI tasks (P300, motor imagery), but also that this model learns features of neural data related to individual variability, and other salient electrophysiological components (e.g., alpha rhythms). In addition to describing and evaluating a novel approach to pre-training BCI models and neural decoding, this work opens the aperture for what kind of tasks and use-cases might exist for neural data in concert with powerful AI methods.