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• W2809182427 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." @default.
• W2809182427 created "2018-06-29" @default.
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• W2809182427 date "2021-01-01" @default.
• W2809182427 modified "2023-10-17" @default.
• W2809182427 title "Inter-Brain EEG Feature Extraction and Analysis for Continuous Implicit Emotion Tagging During Video Watching" @default.
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• W2809182427 doi "https://doi.org/10.1109/taffc.2018.2849758" @default.
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