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Emotion Classification Based on Brain Functional Connectivity Network

Although more and more researchers pay attention to the emotion classification, traditional emotion classification methods can not embrace changes in the global and local areas of the human brain after being stimulated. We propose an emotion classification method based on SVM combining brain functional connectivity. Firstly, the nonlinear phase-locked value (PLV) is used to calculate the multiband brain functional connectivity network, which is then converted into a binary brain network, and seven features of binary brain network are calculated. Secondly, support vector machines (SVM) are used to classify positive and negative emotions at the valence dimension and arousal dimension in the multiband. Experimental results on DEAP show that the best emotion classification accuracy of the proposed method is 86.67% in the arousal dimension, and 84.44% in the valence dimension. The results demonstrate that the classification accuracy of the arousal dimension is better than the valence dimension and the Beta2 frequency band is more suitable for emotion classification. Finally, several findings on brain functional connectivity network is discussed. The left and right areas of brain functional connectivity network are unbalanced in the low frequency band, and the feature values of clustering coefficient, average shortest path length, global efficiency, local efficiency, node degree are positively correlated with the arousal degree in the arousal dimension. Humans emotions are suppressed in the low frequency band, and the brain functional connectivity network after emotional stimulation is strengthened in the high frequency band. Our findings on emotion classification are valuable and consistent with the study of neural mechanisms.