Emotion Measurement Using Biometric Signal
- 1 College of Pharmacy, Kinjo Gakuin University, Nagoya, Japan
- 2 College of Pharmacy, Kinjo Gakuin University, Nagoya, Japan
- 3 College of Pharmacy, Kinjo Gakuin University, Nagoya, Japan
- 4 Department of Pharmacy, Gifu University of Medical Science, Kani, Japan
- 5 Graduate School of Medicine, Aichi Medical University, Nagakute, Japan
- 6 Graduate School of Medicine, Aichi Medical University, Nagakute, Japan
- 7 College of Pharmacy, Kinjo Gakuin University, Nagoya, Japan
- 8 Department of Pharmacy, Nagoya Ekisaikai Hospital, Nagoya, Japan
Abstract
In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the <i>β</i>/<i>θ</i> value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.
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