Research Progress of EEG-Based Emotion Recognition: A Survey

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-05-28 DOI:10.1145/3666002
Yiming Wang, Bin Zhang, Lamei Di
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Abstract

Emotion recognition based on electroencephalography (EEG) signals has emerged as a prominent research field, facilitating objective evaluation of diseases like depression and motion detection for heathy people. Starting from the basic concepts of temporal-frequency-spatial features in EEG and the methods for cross-domain feature fusion. This survey then extends the overfitting challenge of EEG single-modal to the problem of heterogeneous modality modeling in multi-modal conditions. It explores issues such as feature selection, sample scarcity, cross-subject emotional transfer, physiological knowledge discovery, multi-modal fusion methods and modality missing. These findings provide clues for researchers to further investigate emotion recognition based on EEG signals.

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基于脑电图的情绪识别研究进展:调查
基于脑电图(EEG)信号的情绪识别已成为一个突出的研究领域,有助于对抑郁症等疾病进行客观评估和对健康人进行运动检测。本研究从脑电信号的时间-频率-空间特征的基本概念和跨域特征融合方法入手。然后,本研究将脑电图单模态过拟合挑战扩展到多模态条件下的异构模态建模问题。它探讨了特征选择、样本稀缺性、跨主体情感转移、生理知识发现、多模态融合方法和模态缺失等问题。这些发现为研究人员进一步研究基于脑电信号的情感识别提供了线索。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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