Tracking liking state in brain activity while watching multiple movies

Emotion is a valuable information in various applications ranging from human-computer interaction to automated multimedia content delivery. Conventional methods to recognize emotion were based on speech prosody cues, facial expression, and body language. However, this information may not appear when people watch a movie. In recent years, some studies have started to use electroencephalogram (EEG) signals in recognizing emotion. But, the EEG data were entirely analyzed in each scene of movies for emotion classification. Thus, the detailed information of emotional state changes cannot be extracted. In this study, we utilize EEG to track affective state during watching multiple movies. Experiments were done by measuring continuous liking state during watching three types of movies, and then constructing subject dependent emotional state tracking model. We used support vector machine (SVM) as a classifier, and support vector regression (SVR) for regression. As a result, the best classification accuracy was 77.6%, and the best regression model achieved 0.645 of correlation coefficient between actual liking state and predicted liking state. These results demonstrate that continuous emotional state can be predicted by our EEG-based method.