结合多视图特征融合和交叉注意力图卷积的EEG-fNIRS情感识别

doi:10.3778/j.issn.1002-8331.2409-0399

摘要/Abstract

摘要： 为了有效学习脑电（EEG）和功能近红外（fNIRS）信号的情感认知时-频-空域信息，提出一种多视图脑电的多路静动态图卷积交叉注意网络（MF-MSDG-CAFF）方法用于EEG-fNIRS情感识别。通过对情感视频诱发的EEG和fNIRS数据提取各通道信号的不同视图特征及其空间连接关系，构建图结构数据；并行引入静、动态图卷积，捕获不同模态通道间的连接信息和交互特性；通过交叉注意力网络进行特征融合，从而提高情感识别的准确率；结果表明，与单视图EEG相比，提出的多视图EEG方法拥有较高的识别准确率；与仅EEG和仅fNIRS单模态识别结果相比，提出的融合模型的识别率提升1.04和23.72个百分点；与当前常用的EEG-fNIRS融合方法相比，提出的融合模型的识别率提升1.56~15.48个百分点。

关键词: 多视图EEG, 多模态融合, 静动态图卷积神经网络, 交叉注意力, 情感识别

Abstract: In order to obtain the complementary time-frequence-space information of EEG and fNIRS, a multi-view feature fusion and cross-attention map convolution (MF-MSDG-CAFF) method is proposed for EEG-fNIRS emotion recognition. Firstly, the different view features and spatial connections of each channel signal are extracted from EEG and fNIRS data induced by emotional video, and the graph structure data is constructed. Then, the convolution of static and dynamic graphs is introduced in parallel to integrate the connection information between different modal channels and capture the spatial interaction characteristics of brain cognition. Finally, the cross-attention network is used for feature fusion to improve the accuracy of emotion recognition. The results show that compared with single-view EEG, the proposed multi-view EEG method has higher recognition accuracy. Compared with the single mode recognition results of EEG and fNIRS alone, the recognition rate of the proposed fusion model is improved by 1.04 and 23.72 percentage points. Compared with the current EEG-fNIRS fusion methods, the recognition rate of the proposed fusion model is improved by 1.56~15.48 percentage points.

Key words: multi-view EEG, multimodal fusion, static and dynamic graph convolutional neural networks, cross attention, emotion recognition

陶晨曦, 张雪英, 陈桂军. 结合多视图特征融合和交叉注意力图卷积的EEG-fNIRS情感识别[J]. 计算机工程与应用, 2025, 61(24): 187-196.

TAO Chenxi, ZHANG Xueying, CHEN Guijun. EEG-fNIRS Emotion Recognition Combined with Multi-View Feature Fusion and Cross Attention Graph Convolution[J]. Computer Engineering and Applications, 2025, 61(24): 187-196.

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