Self-supervised Learning for Electroencephalogram: A Systematic Survey

doi:10.1145/3736574

	Self-supervised Learning for Electroencephalogram: A Systematic Survey
	Weng, Weining 1,2; Gu, Yang 1,2; Guo, Shuai 1,2; Ma, Yuan 1,2; Yang, Zhaohua 1,2; Liu, Yuchen 1,2; Chen, Yiqiang 1,2
	2025-12-01
发表期刊	ACM COMPUTING SURVEYS
ISSN	0360-0300
卷号	57 期号:12 页码:38
摘要	Electroencephalography (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 and 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 article concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representations and proposes a systematic survey of the SSL for EEG signals. In this article, (1) We introduce the concept and theory of self-supervised learning and typical SSL frameworks. (2) We provide a comprehensive survey of SSL for EEG analysis, including taxonomy, methodology, and technical details of the existing EEG-based SSL frameworks, and discuss the differences between these methods. (3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets, and further explore its application in largescale pre-trained foundation models for EEG signals. (4) Finally, we discuss the potential directions for future SSL-EEG research.
关键词	Self-supervised learning electroencephalogram contrastive learning representation learning
DOI	10.1145/3736574
收录类别	SCI
语种	英语
资助项目	Beijing Municipal Science and Technology Commission[Z221100002722009] ; Improvement Project of Chinese Academy of Sciences ; Youth Innovation Promotion Association CAS
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:001542065100005
出版者	ASSOC COMPUTING MACHINERY
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/41989
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Gu, Yang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Weng, Weining,Gu, Yang,Guo, Shuai,et al. Self-supervised Learning for Electroencephalogram: A Systematic Survey[J]. ACM COMPUTING SURVEYS,2025,57(12):38.
APA	Weng, Weining.,Gu, Yang.,Guo, Shuai.,Ma, Yuan.,Yang, Zhaohua.,...&Chen, Yiqiang.(2025).Self-supervised Learning for Electroencephalogram: A Systematic Survey.ACM COMPUTING SURVEYS,57(12),38.
MLA	Weng, Weining,et al."Self-supervised Learning for Electroencephalogram: A Systematic Survey".ACM COMPUTING SURVEYS 57.12(2025):38.