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.

本文提出了一种针对脑电图（ EEG）信号的自我监督学习（ SSL）框架综述，讨论了典型的 SSL 框架、现有的 EEG-SSL 框架以及 SSL 方法在不同下游任务中的适应性和潜在发展方向。