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Self-supervised Learning for Electroencephalogram: A Systematic Survey

文献类型：期刊论文

作者Weng, Weining1,2; Gu, Yang1,2; Guo, Shuai1,2; Ma, Yuan1,2; Yang, Zhaohua1,2; Liu, Yuchen1,2; Chen, Yiqiang1,2
刊名ACM COMPUTING SURVEYS
出版日期2025-12-01
卷号57期号:12页码:38
关键词Self-supervised learning electroencephalogram contrastive learning representation learning
ISSN号0360-0300
DOI10.1145/3736574
英文摘要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.
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
语种英语
WOS记录号WOS:001542065100005
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/41988]  
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.

入库方式： OAI收割

来源：计算技术研究所

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