关键词: 情感识别, 脑电图, 相位同步, 复杂网络

Abstract: This paper quantizes phase synchronism between two pairs of electrodes by phase locking value, and then constructs an incidence matrix. To find the significance nodes, the research extracts the area under the curve of degree and betweenness centrality in different sparsity of networks as features. In addition, this experiment input these features into a support vector machine to train some classified models. The result indicates that the local features of PLV functional network can effectively distinguish among different types of emotional EEG data, which may provide an effective method of emotional recognition based on EEG data.

Key words: emotional recognition, electroencephalograph, phase synchronism, complex networks