Emotions play an important role in human interaction and decision-making processes. Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion. Emotions are psychologically explained by the valence-arousal model, but as yet, frontal asymmetry has not been fully explained by this model. In this study, we explored frontal asymmetry of emotions based on the valence-arousal model using the same auditory stimulus. Changes in emotional states using self-report questionnaires were investigated before and after the auditory stimulus. Spectral power and weighted phase lag index were calculated in the delta, theta, alpha, beta, and gamma bands. Phase-amplitude coupling was also measured to explore communication among different frequency bands associated with emotions. After the auditory stimulus, alpha power decreased in both left and right frontal regions and the delta-weighted phase lag index in the left-right regions was increased. However, no frontal asymmetry was identified after the auditory stimulus. Additionally, we explored the brain changes according to the valence-arousal model based on emotional states. After the auditory stimulus, frontal asymmetry of alpha power was clearly observed only for negative valence. This finding was possible because subjective emotions were considered despite listening to the same stimulus. Finally, phase-amplitude coupling identified left-hemisphere dominance after the auditory stimulus, regardless of subjective emotions. These results may help us understand frontal asymmetry associated with emotional mechanisms. In addition, these findings can be used directly in the brain-computer interface to improve emotion recognition performance for real-world practical applications.
Bibliographical noteFunding Information:
This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (No. 2017-0-00451; Development of Brain-Computer Interface (BCI) based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Department of Artificial Intelligence, Korea University).
© 2013 IEEE.
- Electroencephalogram (EEG)
- frontal asymmetry
- phase-amplitude coupling (PAC)
- power spectral density (PSD)
- weighted phase lag index (WPLI)
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering