# CoMET: A Contrastive-Masked Brain Foundation Model for Universal EEG Representation

## TL;DR

CoMET is a novel contrastive-masked EEG foundation model that enhances global discriminative features and demonstrates superior universal EEG representation across diverse datasets, showing strong clinical potential.

## Contribution

This paper introduces CoMET, a contrastive-masked EEG foundation model with a redesigned patching strategy and a novel contrastive learning framework, improving global feature discrimination.

## Key findings

- Achieved SOTA results on ten downstream EEG datasets.
- Pre-trained on over one million samples from 3000 subjects.
- Demonstrated strong clinical potential in diverse applications.

## Abstract

Electroencephalography (EEG) is a non-invasive technique for recording brain activity, widely used in brain-computer interfaces, clinic, and healthcare. Traditional EEG deep models typically focus on specific dataset and task, limiting model size and generalization. Recently, self-supervised brain foundation models have emerged and been applied to various downstream tasks. Nevertheless, these models still have limitations: current SOTA models typically rely on masked reconstruction strategy; however, EEG features of adjacent channels are highly correlated, which causes the pre-training to overly focus on low-dimensional signal-similarity features in local regions and neglect the global discriminative patterns vital for downstream tasks. To address these limitations, we propose a brain foundation model called CoMET. Specifically, we employ the masked autoencoder with redesigned patching and embedding for EEG as backbone and devise a novel contrastive learning framework with mirror-scale augmentation to strengthen the global discrimination ability. CoMET is pre-trained on mixed EEG datasets over 3000 subjects with over one million samples. It is evaluated on ten different downstream datasets, and the SOTA results demonstrate CoMET's superior ability in extracting universal EEG representations and strong clinical potential.