Differences in Emotion Signals Based on Gender According to EEG Signal Record

Article Preview

Abstract:

Emotions are complex responses influenced by physiological systems and environmental conditions. Societal stereotypes often depict that women are more emotional than men, although some researchers are still trying to show a clear biological basis for this assumption. Therefore, it is important to explore gender differences in emotional responses from a neurological perspective. This study aims to investigate gender differences in emotional responses using EEG (Electroencephalography) recordings, focusing on Power Spectral Density (PSD) analysis to compare the level of brain activity in men and women during the emotional states of "happy" and "sad", looking at specific frequency ranges. We recorded EEG signals from 16 participants (8 men and 8 women). We calculated PSD feature values for the Alpha, Beta, and Gamma frequency ranges, specifically in the F8 and FP2 channels related to emotional processing. The results showed that women had higher PSD values in the "happy" condition, especially in the F8 and FP2 channels, indicating greater brain activity compared to men. However, in the "sad" condition, women had lower PSD values than men. These findings suggest that women may experience or express happiness with more intense brain activity than men. Implications of this research include the development of more effective cognitive and emotional therapies with a gender-specific approach, as well as a deeper understanding of gender-specific neural mechanisms in emotional responses.

You might also be interested in these eBooks

Info:

* - Corresponding Author

[1] N. Fatih, A. D. Wibawa, M. H. Purnomo, and A. Mas, "Comparative Analysis of EEG-based Emotion Recognition between Male and Female Participants Using Hjorth Parameter," in 2023 International Electronics Symposium (IES), 2023, p.179–185.

DOI: 10.1109/IES59143.2023.10242538

Google Scholar

[2] J. Gross, "Emotion Regulation: Affective, Cognitive, and Social Consequences," Psychophysiology, vol. 39, p.281–291, Jun. 2002.

DOI: 10.1017/S0048577201393198

Google Scholar

[3] R. Levenson, "The Autonomic Nervous System and Emotion," Emotion Review, vol. 6, p.100–112, Mar. 2014.

DOI: 10.1177/1754073913512003

Google Scholar

[4] I. Mauss, R. Levenson, L. McCarter, F. Wilhelm, and J. Gross, "'The Tie That Binds? Coherence Among Emotion Experience, Behavior and Physiology,'" Emotion, vol. 5, p.175–190, Jun. 2005.

DOI: 10.1037/1528-3542.5.2.175

Google Scholar

[5] P. Kuppens, D. Champagne, and F. Tuerlinckx, "The Dynamic Interplay between Appraisal and Core Affect in Daily Life," Front Psychol, vol. 3, p.380, Oct. 2012.

DOI: 10.3389/fpsyg.2012.00380

Google Scholar

[6] R. Lazarus, "Cognition and Motivation in Emotion," Am Psychol, vol. 46, p.352–367, Apr. 1991.

DOI: 10.1037/0003-066X.46.4.352

Google Scholar

[7] J. J. Gross and L. F. Barrett, "Emotion Generation and Emotion Regulation: One or Two Depends on Your Point of View," Emotion Review, vol. 3, p.16–8, 2011, [Online]. Available: https://api.semanticscholar.org/CorpusID:31284527

DOI: 10.1177/1754073910380974

Google Scholar

[8] J. Gross, "Antecedent- and Response-Focused Emotion Regulation: Divergent Consequences for Experience, Expression, and Physiology," J Pers Soc Psychol, vol. 74, p.224–237, Jan. 1998.

DOI: 10.1037/0022-3514.74.1.224

Google Scholar

[9] D. Schacter, D. Gilbert, D. Wegner, and B. Hood, Psychology: Second European Edition. 2016.

DOI: 10.1007/978-1-137-40673-6

Google Scholar

[10] K. Scherer, "Scherer KR. What are emotions? And how can they be measured? Soc Sci Inf 44: 695-729," Social Science Information, vol. 44, p.695–792, Dec. 2005.

DOI: 10.1177/0539018405058216

Google Scholar

[11] M. Grossman, "Sex Differences in Intensity of Emotional Experience: A Social Role Interpretation," J Pers Soc Psychol, vol. 65, p.1010–1022, Nov. 1993.

DOI: 10.1037/0022-3514.65.5.1010

Google Scholar

[12] F. Fujita, E. Diener, and E. Sandvik, "Gender Differences in Negative Affect and Well-Being: The Case for Emotional Intensity," J Pers Soc Psychol, vol. 61, p.427–434, Sep. 1991.

DOI: 10.1037/0022-3514.61.3.427

Google Scholar

[13] L. Brody, "Gender and Emotion: Beyond Stereotypes," Journal of Social Issues, vol. 53, p.369–393, Apr. 2010.

DOI: 10.1111/j.1540-4560.1997.tb02448.x

Google Scholar

[14] L. Barrett, R. Lane, and L. Schwartz, "Sex Differences in Emotional Awareness," Pers Soc Psychol Bull, vol. 26, p.1027–1035, Nov. 2000.

DOI: 10.1177/01461672002611001

Google Scholar

[15] K. J. Anderson and C. Leaper, "Emotion Talk Between Same- and Mixed-Gender Friends: Form and Function," J Lang Soc Psychol, vol. 17, no. 4, p.419–448, Dec. 1998.

DOI: 10.1177/0261927X980174001

Google Scholar

[16] S. Zhang, X. Zhao, and Q. Tian, "Spontaneous Speech Emotion Recognition Using Multiscale Deep Convolutional LSTM," IEEE Trans Affect Comput, vol. 13, no. 2, p.680–688, 2022.

DOI: 10.1109/TAFFC.2019.2947464

Google Scholar

[17] J. Zhang, Z. Yin, P. Chen, and S. Nichele, "Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review," Information Fusion, vol. 59, p.103–126, 2020.

DOI: 10.1016/j.inffus.2020.01.011

Google Scholar

[18] J. A. Russell, "A circumplex model of affect.," J Pers Soc Psychol, vol. 39, no. 6, p.1161–1178, 1980.

DOI: 10.1037/h0077714

Google Scholar

[19] S. M. Alarcão and M. J. Fonseca, "Emotions Recognition Using EEG Signals: A Survey," IEEE Trans Affect Comput, vol. 10, no. 3, p.374–393, 2019.

DOI: 10.1109/TAFFC.2017.2714671

Google Scholar

[20] W. Zhang, A. Song, H. Zeng, B. Xu, and M. Miao, "The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, p.2732–2742, 2022.

DOI: 10.1109/TNSRE.2022.3208312

Google Scholar

[21] S. D. Suryani, A. D. Wibawa, and D. P. Wulandari, "EEG Analysis of Familiar and Unfamiliar Objects Using Wavelet Energy and Shannon Entropy," in 2024 16th International Conference on Knowledge and Smart Technology (KST), 2024, p.226–231.

DOI: 10.1109/KST61284.2024.10499671

Google Scholar

[22] A. D. Wibawa, S. D. Suryani, and S. Pratasik, "Classifying Electroencephalogram (EEG) Signals Via Brain Activity Mapping to Distinguish Identified vs Unidentified Information," CommIT (Communication and Information Technology) Journal, vol. 19, no. 1, Apr. 2025.

DOI: 10.21512/commit.v19i1.12500

Google Scholar

[23] P. Zhong, D. Wang, and C. Miao, "EEG-Based Emotion Recognition Using Regularized Graph Neural Networks," IEEE Trans Affect Comput, vol. 13, no. 3, p.1290–1301, 2022.

DOI: 10.1109/TAFFC.2020.2994159

Google Scholar

[24] M. Aldayel, M. Ykhlef, and A. Alnafjan, "Deep Learning for EEG-Based Preference Classification in Neuromarketing," Applied Sciences, vol. 10, p.1525, Oct. 2020.

DOI: 10.3390/app10041525

Google Scholar

[25] A. Judith, B. P. Sankaralingam, and R. Mahendran, "Artifact Removal from EEG signals using Regenerative Multi-Dimensional Singular Value Decomposition and Independent Component Analysis," Biomed Signal Process Control, vol. 74, p.103452, Apr. 2022.

DOI: 10.1016/j.bspc.2021.103452

Google Scholar

[26] M. Pratiwi, A. D. Wibawa, and M. H. Purnomo, "EEG-based Happy and Sad Emotions Classification using LSTM and Bidirectional LSTM," in 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA), 2021, p.89–94.

DOI: 10.1109/ICERA53111.2021.9538698

Google Scholar

[27] D. B. Percival and A. T. Walden, "Spectral analysis for physical applications : multitaper and conventional univariate techniques," Technometrics, vol. 38, p.294, 1996, [Online]. Available: https://api.semanticscholar.org/CorpusID:123118575.

DOI: 10.2307/1270624

Google Scholar

[28] U. M. Nater, E. Abbruzzese, M. Krebs, and U. Ehlert, "Sex differences in emotional and psychophysiological responses to musical stimuli," International Journal of Psychophysiology, vol. 62, no. 2, p.300–308, 2006.

DOI: 10.1016/j.ijpsycho.2006.05.011

Google Scholar

[29] Y.-Y. Lee and S. Hsieh, "Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns," PLoS One, vol. 9, no. 4, pp. e95415-, Apr. 2014, [Online]. Available:.

DOI: 10.1371/journal.pone.0095415

Google Scholar