Knowledge-guided EEG Representation Learning
This work addresses the need for robust self-supervised methods in EEG analysis, which is crucial for medical and research applications due to limited labeled data, representing an incremental advancement in domain-specific adaptation.
The authors tackled the problem of adapting self-supervised learning to EEG biosignals by proposing a knowledge-guided pre-training objective and a state space-based architecture, resulting in improved representation learning, downstream performance, and reduced pre-training data requirements compared to prior works.
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.