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

• Published in: IEEE transactions on affective computing Vol. 12; no. 1; pp. 92 - 102
• Main Authors: Ding, Yue, Hu, Xin, Xia, Zhenyi, Liu, Yong-Jin, Zhang, Dan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Affective computing; Amplitudes; Arousal; Brain; Correlation; EEG; Electroencephalography; Emotion; Emotions; Feature extraction; Frequencies; implicit tagging; inter-brain; Marking; Multimedia; Multimedia communication; Real time; Real-time systems; Regression analysis; Streaming media; Tagging
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2018.2849758

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.