%0 Journal Article
%A LIU Yongli
%A MA Qingmin
%A CHAO Hao
%T EEG-Based Emotion Recognition by Using Convolutional Echo-State Network
%D 
%R 10.13190/j.jbupt.2021-078
%J Journal of Beijing University of Posts and Telecommunications
%P 36-43
%V 45
%N 2
%X A convolutional echo-state network (CESN) model is proposed for the emotion recognition task based on electroencephalogram (EEG) signals. First, a feature matrix sequence of EEG signal is constructed. Then, high-level abstract features are extracted from the feature matrices via convolution, and one dimension feature vectors are formed. After that, a reservoir with self-feedback function is employed to extract the dynamic temporal information from the feature vector sequence. Finally, the emotion recognition task is realized by ridge regression. The experiment is carried out on the database for emotion analysis using physiological signals datasets. The experiment result shows that the EEG signal segments contain temporal information related to emotion, and the CESN model can mine and utilize the information effectively. In addition, the proposed CESN model can circumvent the problems of the local optimization and long training time, which are caused by back propagation algorithm in convolutional neural network.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-078