Self-supervised EEG Representation Learning for Automatic Sleep Staging

1 code implementation • 27 Oct 2021 • Chaoqi Yang, Danica Xiao, M. Brandon Westover, Jimeng Sun

Objective: In this paper, we aim to learn robust vector representations from massive unlabeled EEG signals, such that the learned vectorized features (1) are expressive enough to replace the raw signals in the sleep staging task; and (2) provide better predictive performance than supervised models in scenarios of fewer labels and noisy samples.

EEG Representation Learning +2