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 …

Metadata

publication

arXiv preprint arXiv:2403.03222, 2024

year

2024

publication date

2024/7/15

authors

Aditya Kommineni, Kleanthis Avramidis, Richard Leahy, Shrikanth Narayanan

link

https://ieeexplore.ieee.org/abstract/document/10782310/

resource_link

https://arxiv.org/pdf/2403.03222

conference

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

pages

1-6

publisher

IEEE