Inter-Brain EEG Feature Extraction and Analysis for Continuous Implicit Emotion Tagging During Video Watching

Abstract

How to efficiently tag the emotional experience of multimedia contents is an important and challenging problem in the field of affective computing. This paper presents an EEG-based real-time emotion tagging approach, by extracting inter-brain features from a group of participants when they watch the same emotional video clips. First, the continuous subjective reports on both the arousal and valence dimensions of emotion were obtained by employing a three-round behavioral rating paradigm. Second, the inter-brain features were systematically explored in both spectral and temporal domain. Finally, regression analyses were performed to evaluate the effectiveness of inter-brain amplitude and phase features. The inter-brain amplitude feature showed significantly better prediction performance than the inter-brain phase feature, as well as another two conventional features (spectral power and inter-subject correlation). By combining the four types of features, regression values (R2) were obtained for the prediction of arousal (0.61 + 0.01) and valence (0.70 + 0.01), corresponding to prediction errors of 1.01 + 0.02 and 0.78 + 0.02 (unit on 9-point scales), respectively. The contributions of different electrodes and frequency bands were also analyzed. Our results show promising potentials of inter-brain EEG features in real-time emotion tagging applications.