Knowledge-guided EEG Representation Learning

arXiv:2403.03222v1 Announce Type: new
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 …

abstract arxiv audio cs.ai cs.lg data domain domains eeg eess.sp knowledge learn multimedia paradigm representation representation learning results robust scale self-supervised learning speech supervised learning type vision