mulEEG: A Multi-View Representation Learning on EEG Signals

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 unsuper...

Full description

Saved in:
Bibliographic Details
Main Authors Kumar, Vamsi, Reddy, Likith, Sharma, Shivam Kumar, Dadi, Kamalakar, Yarra, Chiranjeevi, Raju, Bapi S, Rajendran, Srijithesh
Format Journal Article
LanguageEnglish
Published 07.04.2022
Subjects
Computer Science - Learning
Online AccessGet full text

Cover

More Information
Summary: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.
DOI:10.48550/arxiv.2204.03272