Hongjun Zhang

Self-Supervised Learning for Sleep Stage Classification with Predictive and Discriminative Contrastive Coding

The purpose of this paper is to learn efficient representations from raw electroencephalogram (EEG) signals for sleep stage classification via self-supervised learning (SSL). Although supervised methods have gained favorable performance, they heavily rely on manually labeled datasets. Recently, SSL…