EEG2Rep: Enhancing Self-supervised EEG Representation Through Informative Masked Inputs

arXiv:2402.17772v1 Announce Type: cross
Abstract: Self-supervised approaches for electroencephalography (EEG) representation learning face three specific challenges inherent to EEG data: (1) The low signal-to-noise ratio which challenges the quality of the representation learned, (2) The wide range of amplitudes from very small to relatively large due to factors such as the inter-subject variability, risks the models to be dominated by higher amplitude ranges, and (3) The absence of explicit segmentation in the continuous-valued sequences which can result in less informative …

arxiv cs.lg eeg eess.sp inputs representation through type