| LI Xueqing,LI Penghai,FANG Zhendong,CHENG Longlong,WANG Zhiyong,WANG Weijie.Research on EEG emotion recognition based on CNN+BiLSTM+self-attention model[J].Optoelectronics Letters,2023,(8):498-505 |
| Research on EEG emotion recognition based on CNN+BiLSTM+self-attention model |
| Author Name | Affiliation | | LI Xueqing | School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China | | LI Penghai | School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China | | FANG Zhendong | China France BOHAI Geoservices Co., Ltd., Tianjin 300457, China | | CHENG Longlong | China Electronics Cloud Brain Tianjin Technology Co., Ltd., Tianjin, China | | WANG Zhiyong | School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China | | WANG Weijie | Institute of Motion Analysis and Research, University of Dundee, Scotland DD14HN, UK |
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| Abstract: |
| 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+BiLSTM+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 (BiLSTM) 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. |
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