Self-supervised Contrastive Learning for EEG-based Sleep Staging

TL;DR

This paper introduces a self-supervised contrastive learning approach for EEG-based sleep staging, leveraging unlabeled data to improve classification accuracy and robustness, especially when labeled data is scarce.

Contribution

It proposes a novel SSL method for EEG sleep staging that enhances feature learning and generalization by using transformation-based pretext tasks.

Findings

Achieved 88.16% accuracy on Sleep-edf dataset

Demonstrated robustness with limited labeled data

Validated effectiveness of SSL in EEG analysis

Abstract

EEG signals are usually simple to obtain but expensive to label. Although supervised learning has been widely used in the field of EEG signal analysis, its generalization performance is limited by the amount of annotated data. Self-supervised learning (SSL), as a popular learning paradigm in computer vision (CV) and natural language processing (NLP), can employ unlabeled data to make up for the data shortage of supervised learning. In this paper, we propose a self-supervised contrastive learning method of EEG signals for sleep stage classification. During the training process, we set up a pretext task for the network in order to match the right transformation pairs generated from EEG signals. In this way, the network improves the representation ability by learning the general features of EEG signals. The robustness of the network also gets improved in dealing with diverse data, that is, extracting constant features from changing data. In detail, the network's performance depends on the choice of transformations and the amount of unlabeled data used in the training process of self-supervised learning. Empirical evaluations on the Sleep-edf dataset demonstrate the competitive performance of our method on sleep staging (88.16% accuracy and 81.96% F1 score) and verify the effectiveness of SSL strategy for EEG signal analysis in limited labeled data regimes. All codes are provided publicly online.