Knowledge-guided EEG Representation Learning

TL;DR

This paper introduces a self-supervised EEG representation learning model that leverages knowledge-guided pre-training and state space-based architecture, achieving robust, efficient embeddings with less data.

Contribution

It proposes a novel knowledge-guided pre-training objective tailored for EEG signals and demonstrates improved performance and data efficiency over prior methods.

Findings

Enhanced EEG embedding representations.

Reduced pre-training data requirements.

Improved downstream task performance.

Abstract

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosignals, owing to the scarcity of labelled data in such scenarios. The ability to leverage large-scale unlabelled data to learn robust representations could help improve the performance of numerous inference tasks on biosignals. Given the inherent domain differences between multimedia modalities and biosignals, the established objectives for self-supervised learning may not translate well to this domain. Hence, there is an unmet need to adapt these methods to biosignal analysis. In this work we propose a self-supervised model for EEG, which provides robust performance and remarkable parameter efficiency by using state space-based deep learning architecture. We also propose a novel knowledge-guided pre-training objective that accounts for the idiosyncrasies of the EEG signal. The results indicate improved embedding representation learning and downstream performance compared to prior works on exemplary tasks. Also, the proposed objective significantly reduces the amount of pre-training data required to obtain performance equivalent to prior works.