  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.
