mulEEG: A Multi-view Representation Learning on EEG Signals

• Published in: Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 Vol. 13433; pp. 398 - 407
• Main Authors: Kumar, Vamsi, Reddy, Likith, Kumar Sharma, Shivam, Dadi, Kamalaker, Yarra, Chiranjeevi, Bapi, Raju S., Rajendran, Srijithesh
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: Multi-view learning; Self-supervised; Sleep staging
• ISBN: 3031164369; 9783031164361
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-16437-8_38

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 (Code Available at: https://github.com/likith012/mulEEG).