mulEEG: A Multi-View Representation Learning on EEG Signals

TL;DR

mulEEG is a novel self-supervised learning method that leverages multiple views of EEG signals to learn superior representations for sleep-staging, outperforming supervised and baseline methods.

Contribution

The paper introduces a new multi-view self-supervised approach for EEG representation learning that effectively utilizes complementary information across views.

Findings

Outperforms supervised training on sleep-staging tasks

Surpasses multi-view baseline methods in transfer learning

Learns better representations using diverse loss and multi-view data

Abstract

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.