Research on EEG emotion recognition based on CNN+BiLSTM+self-attention model

查看全文 作　　者：LI [1]Xueqing;LI [1]Penghai;FANG [2]Zhendong;CHENG [3]Longlong;WANG [1]Zhiyong;WANG [4]Weijie

机构地区：[1]School of Integrated Circuit Science and Engineering,Tianjin University of Technology,Tianjin 300384,China;[2]China France BOHAI Geoservices Co.,Ltd.,Tianjin 300457,China;[3]China Electronics Cloud Brain(Tianjin)Technology Co.,Ltd.,Tianjin,China;[4]Institute of Motion Analysis and Research,University of Dundee,Scotland DD14HN,UK

出　　处：《Optoelectronics Letters》索引2023年第19卷第8期,共7页

基　　金：supported by the National Key Research and Development Program of China (No.2021YFF1200600);the National Natural Science Foundation of China (No.61806146);the Natural Science Foundation of Tianjin City (Nos.18JCYBJC95400 and 19JCTPJC56000)。 摘　　要：To address the problems of insufficient dimensionality of electroencephalogram(EEG) feature extraction,the tendency to ignore the importance of different sequential data segments,and the poor generalization ability of the model in EEG based emotion recognition,the model of convolutional neural network and bi-directional long short-term memory and self-attention(CNN+Bi LSTM+self-attention) is proposed.This model uses convolutional neural network(CNN) to extract more distinctive features from both spatial and temporal dimensions.The bi-directional long short-term memory(Bi LSTM) is used to further preserve the long-term dependencies between the temporal phases of sequential data.The self-attention mechanism can change the weights of different channels to extract and highlight important information and address the often-ignored importance of different channels and samples when extracting EEG features.The subject-dependent experiment and subject-independent experiment are performed on the database for emotion analysis using physiological signals(DEAP) and collected datasets to verify the recognition performance.The experimental results show that the model proposed in this paper has excellent recognition performance and generalization ability. 关键词：EEG WEIGHTS LSTM

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Research on EEG emotion recognition based on CNN+BiLSTM+self-attention model

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