数据采集与处理2024，Vol.39Issue(6)：1543-1552,10.DOI:10.16337/j.1004-9037.2024.06.021
基于多域信息融合的卷积Transformer脑电情感识别模型
Convolutional Transformer EEG Emotion Recognition Model Based on Multi-domain Information Fusion
摘要
Abstract
Current emotion recognition methods for eletroencephalogram(EEG)signals seldom fuse spatial,temporal and frequency information,and most methods can only extract local EEG features,resulting in limitations in global information correlation.The article proposes an EEG emotion recognition method based on 3D-CNN-Transformer mechanism(3D-CTM)model with multi-domain information fusion.The method first designs a 3D feature structure based on the characteristics of EEG signals,simultaneously fusing the spatial,temporal,and frequency information of EEG signals.Then a convolutional neural network module is used to learn the deep features for multi-domain information fusion,and then the Transformer self-attention module is connected to extract the global correlations within the feature information.Finally,the global average pooling is used to integrate the feature information for classification.Experimental results show that the 3D-CTM model achieves an average accuracy of 96.36% in the SEED dataset for triple classification and 87.44% in the SEED-Ⅳ dataset for quadruple classification,which effectively improves the emotion recognition accuracy.关键词
脑电信号/情感识别/卷积神经网络/Transformer/自注意力Key words
electroencephalogram(EEG)/emotion recognition/convolutional neural network/Transformer/self-attention分类
信息技术与安全科学引用本文复制引用
张学军,王天晨,王泽田..基于多域信息融合的卷积Transformer脑电情感识别模型[J].数据采集与处理,2024,39(6):1543-1552,10.基金项目
国家自然科学基金(61977039). （61977039）
