Employing subjects' information as privileged information for emotion recognition from EEG signals

Current research of emotion recognition from electroencephalogram (EEG) signals rarely considers common patterns embodied in multiple subjects and individual patterns for each subject simultaneously. Therefore, in this paper, we propose a novel emotion recognition approach using subjects or subject groups as privileged information, which is only available during training. First, five frequency features are extracted from each channel of the EEG signals, and features are selected by statistical tests. Then, we propose two three-node Bayesian networks to capture the joint probability distribution function of emotion labels, EEG features, and subjects or subject groups during training. Through the learned joint probability distribution, the Bayesian networks model both common and individual emotion patterns simultaneously. During testing, emotion labels can be estimated from EEG features only by marginalized over the privileged information, i.e. subjects or subject groups. Experimental results on three benchmark databases, i.e. the MAHNOB-HCI database, the DEAP database and the USTC-ERVS database, demonstrate that our approach incorporating subjects and clusters achieves better emotion recognition performance than training a classifier for each subject, as well as training a classifier without subject information on the whole dataset.