Self-supervised Learning for Electroencephalogram: A Systematic Survey

TL;DR

This systematic survey reviews how self-supervised learning techniques are applied to EEG signals, addressing label scarcity and variability issues to improve EEG analysis through representation learning.

Contribution

The paper provides a comprehensive taxonomy, methodology review, and analysis of SSL frameworks for EEG, highlighting their adaptation to various downstream tasks and future research directions.

Findings

SSL frameworks improve EEG representation learning

Different SSL methods vary in effectiveness across tasks

Future directions include tailored SSL models for EEG

Abstract

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.