面向情绪识别任务的EEG特征提取研究综述

doi:10.3969/j.issn.0258-8021.2025.04.009

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摘要情绪作为个体对客观环境的主观心理和生理反应体系,在人机交互场景中扮演着关键调节角色。情绪识别技术在医学、教育、心理学和军事等多个交叉学科领域具有重要应用价值。相较于面部表情、语音语调和肢体动作等易受主观调控的非生理信号,生理信号具有更高的客观性和真实性。其中,脑电信号(EEG)作为典型的生理信号,在采集便捷性和识别准确性方面具有一定优势,已成为情绪识别领域的重要研究对象。系统综述了基于EEG信号的情绪识别特征提取方法及其研究进展,梳理了包括时域和频域特征、空间域特征、脑网络特征、浅层非线性和流形特征的多维分析框架,重点分析了基于深度学习的自动特征提取方法的技术演进,从多模态融合、个体差异校正以及实时解码等多维度展望了未来技术发展趋势。

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	李蒙蒙
	薛文博
	刘云扬
	何雨碟
	岳彩通
	李志辉
	尚志刚

关键词 ：脑电, 情绪识别, 特征提取, 深度学习

Abstract：Emotion plays a crucial role in human-computer interaction because it makes psychological and physiological responses to the environment. Accurate emotion recognition is vital for applications in the fields of medicine, education, psychology, and military. Compared to non-physiological signals, such as facial expressions and movements, physiological signals are difficult to disguise. As a type of physiological signal, electroencephalogram (EEG) offers advantages in terms of collectionconvenience and recognition accuracy and thus is often used in the field of emotion recognition. This review summarized recent progresses made in the feature extraction for EEG-based emotion recognition, introduced conventional time-frequency features, spatial domain features, brain network features, shallow nonlinear and manifold features, as well as deep learning-based feature extraction methods. Furthermore, this review provided an outlook on future directions.

Key words： EEG emotion recognition feature extraction deep learning

收稿日期: 2024-11-11

PACS:

R318

基金资助:河南省科技攻关项目(252102311095);国家资助博士后研究人员计划项目(GZC20232447);国家自然科学基金(62301496)

通讯作者: ^*E-mail: yuecaitong@zzu.edu.cn

引用本文:

李蒙蒙, 薛文博, 刘云扬, 何雨碟, 岳彩通, 李志辉, 尚志刚. 面向情绪识别任务的EEG特征提取研究综述[J]. 中国生物医学工程学报, 2025, 44(4): 465-477.
Li Mengmeng, Xue Wenbo, Liu Yunyang, He Yudie, Yue Caitong, Li Zhihui, Shang Zhigang. A Review of EEG Feature Extraction for Emotion Recognition Tasks. Chinese Journal of Biomedical Engineering, 2025, 44(4): 465-477.

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http://cjbme.csbme.org/CN/10.3969/j.issn.0258-8021.2025.04.009 或 http://cjbme.csbme.org/CN/Y2025/V44/I4/465

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