Emotion Recognition from EEG Signals using Hierarchical Bayesian Network with Privileged Information

Zhen Gao, Shangfei Wang

Abstract: Current work of emotion recognition from electroencephalogram (EEG) signals mainly focuses on the generality among users, ignoring users' specificity. However, users' emotion is a subjective phenomenon with both common and specific characteristics. Therefore, we propose a novel emotion recognition method using hierarchical Bayesian network to handle generality and specificity of emotions simultaneously. Specifically, by modeling the prior distributions of parameters for each subject, the classifier for a subject is learned together with those of others, with a shared representation. In addition, by marginalizing over the node of subjects, the subject information is used as privileged information, which is only required during training to build a better classifier. Experimental results on the MAHNOB-HCI and DEAP databases demonstrate that our model with the subject id as privileged information can improve the emotion recognition performance.