基于非对称空间特征的脑电信号情感分析研究

计算机工程与科学 ›› 2025, Vol. 47 ›› Issue (5): 921-930.

基于非对称空间特征的脑电信号情感分析研究

王莹1,2,3，杨青1,2,3，王翔宇1,2,3，张勇1,2,3

(1.华中师范大学计算机学院，湖北武汉 430079；2.华中师范大学国家语言资源监测与研究网络媒体中心，湖北武汉 430079；
3.华中师范大学人工智能与智慧学习湖北省重点实验室，湖北武汉 430079）

收稿日期:2023-12-11 修回日期:2024-07-22 出版日期:2025-05-25 发布日期:2025-05-27
基金资助:
国家自然科学基金（61977032）

Research on EEG signal emotion analysis based on asymmetric spatial features

WANG Ying1,2,3，YANG Qing 1,2,3，WANG Xiangyu 1,2,3,ZHANG Yong1,2,3

(1.School of Computer Science,Central China Normal University,Wuhan 430079;
2.National Language Resources Monitor and Research Center for Network Media,
Central China Normal University,Wuhan 430079;
3.Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,
Central China Normal University,Wuhan 430079,China)

Received:2023-12-11 Revised:2024-07-22 Online:2025-05-25 Published:2025-05-27

摘要/Abstract

摘要： 大脑的不对称性会对脑电情感分析产生影响，但是目前很多研究对此特性缺少考虑。结合大脑空间的不对称性，提出了一种混合模型，该模型利用多尺度卷积神经网络提取大脑左右不对称的脑电空间特征，然后使用双向长短期记忆神经网络提取时序特征，最后，通过多头自注意力机制学习特征之间的关系。该模型在公开的DEAP数据集上进行实验验证，唤醒维度分类准确率和F1值分别为93.11%和93.46%，效价维度分类准确率和F1值分别为92.12%和93.27%。该模型在公开的MAHNOB-HCI数据集上进行实验验证，唤醒维度分类准确率和F1值分别为98.58%和97.98%，效价维度分类准确率和F1值分别为98.76%和98.25%。结果表明，在脑电情感识别上该模型具有一定优势，同时通过消融实验证明了非对称空间层的重要性。

关键词: 脑电情感识别, 非对称空间特征, 多尺度卷积神经网络, 双向长短期记忆神经网络, 多头自注意力机制

Abstract: The asymmetry of the brain will have an impact on EEG emotion analysis,but many studies have not considered this property.Combined with the asymmetry of brain space,this paper proposes a hybrid model,which uses multi-scale convolutional neural network to extract the EEG spatial features of left and right asymmetry of the brain,then uses bidirectional long short-term memory neural network to extract temporal features,and finally learns the relationship between features through the multi-head self-attention mechanism.The proposed model is experimentally validated on the publicly available DEAP dataset.The accuracy and F1-score for classifying the arousal dimension are 93.11% and 93.46%, respectively,while those for the valence dimension are 92.12% and 93.27%.Furthermore,the model is validated on the publicly available MAHNOB-HCI dataset,achieving accuracy and F1-score of 98.58% and 97.98% for the arousal dimension,and accuracy and F1-score of 98.76% and 98.25% for the valence dimension.The results demonstrate that the proposed model exhibits certain advantages in EEG-based emotion recognition.Furthermore,ablative experiments confirm the significance of the asymmetrical spatial layer.

Key words: EEG emotion recognition, asymmetric spatial feature, multi-scale convolutional neural network, bidirectional long short-term memory neural network, multi-head self-attention mechanism

王莹, 杨青, 王翔宇, 张勇, . 基于非对称空间特征的脑电信号情感分析研究[J]. 计算机工程与科学, 2025, 47(5): 921-930.

WANG Ying, YANG Qing , WANG Xiangyu , ZHANG Yong, . Research on EEG signal emotion analysis based on asymmetric spatial features[J]. Computer Engineering & Science, 2025, 47(5): 921-930.