Abstract: In recent years,research on EEG signals applied to emotion recognition has received extensive attention,and mapping time series into visibility graph representation can effectively perform EEG emotion recognition through the metric of edges and nodes of the visibility graph.Traditional visibility graph algorithms ignore the correlation between multi-channel EEG signals,and it is difficult to retain the complex feature information on the time series.Therefore,this paper proposes a method to extract EEG signal features from multiple directed weighted visibility graphs and use EEG signal features for emotion recognition.Firstly,the EEG signal is converted into a directed weighted network graph to enhance the feature representation of the signal,the complex network structure is characterized using weighted clustering coefficients,the EEG connection matrix is established with multiple complex networks,and finally the convolutional neural network is used for feature learning,and the emotion recognition results are obtained through learning.The complex network model constructed by multiple directed weighted visibility maps achieves 93.85% accuracy in public dataset validation,which is better than the existing traditional visibility graph methods,and the multiple weighted visibility graph improves the emotion recognition accuracy by 9.4% compared with univariate visibility graph.Experimental results show that the method is also applicable to cross subject data and has good robustness.

Key words: Emotion recognition, EEG signal, Multiple visible graph, Convolutional neural network, Clustering coefficient