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 β/θ 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.
References
[1]
Dzedzickis, A., Kaklauskas, A. and Bucinskas, V. (2020) Human Emotion Recognition: Review of Sensors and Methods. Sensors, 20, 592. https://doi.org/10.3390/s20030592
[2]
Hama, H., Suzuki, N., Hama, Y., Umemoto, T. and Ooyama, T. (2001) Invitation to Emotional Psychology: An Approach to Feelings and Emotions [Translated from Japanese.]. SAIENSU-SHA, Tokyo.
[3]
Miyata, Y. (1996) Brain and Mind [Translated from Japanese.]. Baifukan, Tokyo.
[4]
Fujinaga, H. (2003) Heart Rate Fluctuation and Affect. The Wakayama Economic Review, 314, 23-57.
[5]
Miyagi, Y., Gocho, S., Yamaguchi, N., Miyachi, Y., Nakayama, C., et al. (2023) Predicting the Effect of Pharmacist’s Communication with Patients: Medical Communication Analysis Using Facial Responses. Journal of Pharmaceutical Health Services Research, 14, 221-227. https://doi.org/10.1093/jphsr/rmad029
[6]
Okuma, T., Matsuoka, H. and Ueno, T. (2006) Electroencephalogram Decoding Step by Step Introduction [Translated from Japanese.]. Igaku Shoin, Tokyo.
[7]
Katoh, Z. and Okubo, T. (2006) How to Measure Biological Functions for Beginners [Translated from Japanese.]. Japan Publication Service, Tokyo.
[8]
Lubar, J.F. (1991) Discourse on the Development of EEG Diagnostics and Biofeedback for Attention-Deficit/Hyperactivity Disorders. Biofeedback and Self-Regulation, 16, 201-225. https://doi.org/10.1007/BF01000016
[9]
Hayashi, H. (1999) Clinical Application of Heart Rate Variability—Physiological Significance, Pathological Evaluation, and Prognostic Prediction [Translated from Japanese.]. Igaku Shoin, Tokyo.
[10]
Shaffer, F. and Ginsberg, J.P. (2017) An Overview of Heart Rate Variability Metrics and Norms. Front Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258
[11]
Koelstra, S., Muhl, C., Soleymani, M., Jong-Seok, L., Yazdani, A., et al. (2012) DEAP: A Database for Emotion Analysis; Using Physiological Signals. IEEE Transactions on Affective Computing, 3, 18-31. https://doi.org/10.1109/T-AFFC.2011.15
[12]
Russell, J.A. (1980) A Circumplex Model of Affect. Journal of Personality and Social Psychology, 39, 1161-1178. https://doi.org/10.1037/h0077714
[13]
Feldman Barrett, L. and Russell, J.A. (1998) Independence and Bipolarity in the Structure of Current Affect. Journal of Personality and Social Psychology, 74, 967-984. https://doi.org/10.1037/0022-3514.74.4.967
[14]
Russell, J.A. and Barrett, L.F. (1999) Core Affect, Prototypical Emotional Episodes, and Other Things Called Emotion. Journal of personality and Social Psychology, 76, 805-819. https://doi.org/10.1037/0022-3514.76.5.805
[15]
Marchewka, A., Żurawski, Ł., Jednoróg, K. and Grabowska, A. (2013) The Nencki Affective Picture System (NAPS): Introduction to a Novel, Standardized, Wide-Range, High-Quality, Realistic Picture Database. Behavior Research Methods, 46, 596-610. https://doi.org/10.3758/s13428-013-0379-1
[16]
Peter, C. and Herbon, A. (2006) Emotion Representation and Physiology Assignments in Digital Systems. Interacting with Computers, 18, 139-170. https://doi.org/10.1016/j.intcom.2005.10.006
[17]
Wager, T.D., Kang, J., Johnson, T.D., Nichols, T.E., Satpute, A.B., et al. (2015) A Bayesian Model of Category-Specific Emotional Brain Responses. PLOS Computational Biology, 11, e1004066. https://doi.org/10.1371/journal.pcbi.1004066
[18]
Brouwer, A.-M., Van Wouwe, N., Mühl, C., Van Erp, J. and Toet, A. (2013) Perceiving Blocks of Emotional Pictures and Sounds: Effects on Physiological Variables. Frontiers in Human Neuroscience, 7, 295. https://doi.org/10.3389/fnhum.2013.00295
