
Brain-Computer Interface Application in Recognition and Regulation of Emotion
- 1 School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai, China
* Author to whom correspondence should be addressed.
Abstract
This paper investigates the application of Brain-Computer Interface (BCI) technology in the realms of emotion recognition and regulation. BCI technology facilitates direct communication between the brain and external devices, offering significant promise for improving human-environment interactions, particularly with regard to the identification and modulation of emotional states. By analyzing brain signals, such as electroencephalography, this study classifies emotions based on widely recognized models, including Ekman’s model and the Russell circumplex model. To enhance the precision of emotion classification, machine learning algorithms, such as support vector machines and neural networks, are utilized. Moreover, this study explores BCI’s potential in emotion regulation, focusing on neurofeedback and brain stimulation methods like transcranial direct current stimulation, which have shown therapeutic potential, particularly for disorders related to emotional dysregulation. Additionally, the paper delves into the integration of BCI with virtual reality to create immersive environments conducive to emotional therapy. Despite its considerable potential, BCI technology faces obstacles such as low data transmission rates and the complexities associated with user training. Nonetheless, the integration of BCI technology within Industry 4.0 frameworks holds promising opportunities for optimizing human-machine interactions and improving workplace safety.
Keywords
Brain-computer interface, Emotion recognition, Emotion regulation.
[1]. Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R., & Gorzelanczyk, E. J. (2021). Summary of over Fifty Years with Brain-Computer Interfaces-A Review. Brain Sci, 11(1), 43. https://doi.org/10.3390/brainsci11010043
[2]. Figeys, M., Villarey, S., Leung, A. W., Raso, J., Buchan, S., Kammerer, H., ... & Kim, E. S. (2022). tDCS over the left prefrontal cortex improves mental flexibility and inhibition in geriatric inpatients with symptoms of depression or anxiety: A pilot randomized controlled trial. Front. Rehabil. Sci., 3, 997531. https://doi.org/10.3389/fresc.2022.997531
[3]. Young, M. J., Lin, D. J., & Hochberg, L. R. (2021). Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation. Semin Neurol, 41(2), 206–216. https://doi.org/10.1055/s-0041-1725137
[4]. Maiseli, B., Abdalla, A. T., Massawe, L. V., Mbise, M., Mkocha, K., Nassor, N. A., Ismail, M., Michael, J., & Kimambo, S. (2023). Brain-computer interface: trend, challenges, and threats. Brain Inf, 10(1), 20. https://doi.org/10.1186/s40708-023-00199-3
[5]. Peksa, J., & Mamchur, D. (2023). State-of-the-Art on Brain-Computer Interface Technology. Sensors, 23(13), 6001. https://doi.org/10.3390/s23136001
[6]. Houssein, E. H., Hammad, A., & Ali, A. A. (2022). Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review. Neural Comput&Applic, 34(15), 12527-12557. https://doi.org/10.1007/s00521-022-07292-4
[7]. Alhalaseh, R., & Alasasfeh, S. (2020). Machine-learning-based emotion recognition system using EEG signals. Computers, 9(4), 95. https://doi.org/10.3390/computers9040095
[8]. Wang, X., Ren, Y., Luo, Z., He, W., Hong, J., & Huang, Y. (2023). Deep learning-based EEG emotion recognition: Current trends and future perspectives. Front. Psychol, 14, 1126994. https://doi.org/10.3389/fpsyg.2023.1126994
[9]. Morgenroth, E., Vilaclara, L., Muszynski, M., Gaviria, J., Vuilleumier, P., & Van De Ville, D. (2023). Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI. Social Cognitive and Affective Neuroscience, 18(1), nsad063. https://doi.org/10.1093/scan/nsad063
[10]. Paley, B., & Hajal, N. J. (2022). Conceptualizing Emotion Regulation and Coregulation as Family-Level Phenomena. Clin Child Fam Psychol Rev, 25(1), 19–43. https://doi.org/10.1007/s10567-022-00378-4
[11]. He, Z., Li, Z., Yang, F., Wang, L., Li, J., Zhou, C., & Pan, J. (2020). Advances in Multimodal Emotion Recognition Based on Brain-Computer Interfaces. Brain Sci, 10(10), 687. https://doi.org/10.3390/brainsci10100687
[12]. Jubair, H., Islam, M., Mehenaz, M., Akter, F., & yeasmin, N. (2024). Neurofeedback: applications, advancements, and future directions. https://doi.org/10.21203/rs.3.rs-4842929/v1
[13]. Qiu, X., He, Z., Cao, X., & Zhang, D. (2023). Transcranial magnetic stimulation and transcranial direct current stimulation affect explicit but not implicit emotion regulation: a meta-analysis. Behav Brain Funct, 19(1), 15. https://doi.org/10.1186/s12993-023-00217-8
[14]. Erat, K., Şahin, E. B., Doğan, F., Merdanoğlu, N., Akcakaya, A., & Durdu, P. O. (2024). Emotion recognition with EEG-based brain-computer interfaces: a systematic literature review. Multimed Tools Appl, 1-48. https://doi.org/10.1007/s11042-024-18259-z
[15]. Crowell, A. L., Riva-Posse, P., Holtzheimer, P. E., Garlow, S. J., Kelley, M. E., Gross, R. E., Denison, L., Quinn, S., & Mayberg, H. S. (2019). Long-Term Outcomes of Subcallosal Cingulate Deep Brain Stimulation for Treatment-Resistant Depression. American journal of Psychiatry, 176(11), 949–956. https://doi.org/10.1176/appi.ajp.2019.18121427
[16]. Wen, D., Fan, Y., Hsu, S. H., Xu, J., Zhou, Y., Tao, J., Lan, X., & Li, F. (2021). Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review. Annals of physical and rehabilitation medicine, 64(1), 101404. https://doi.org/10.1016/j.rehab.2020.03.015
[17]. Rawnaque, F. S., Rahman, K. M., Anwar, S. F., Vaidyanathan, R., Chau, T., Sarker, F., & Mamun, K. A. A. (2020). Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf, 7(1), 10. https://doi.org/10.1186/s40708-020-00109-x
[18]. Mridha, M. F., Das, S. C., Kabir, M. M., Lima, A. A., Islam, M. R., & Watanobe, Y. (2021). Brain-Computer Interface: Advancement and Challenges. Sensors, 21(17), 5746. https://doi.org/10.3390/s21175746
[19]. Värbu, K., Muhammad, N., & Muhammad, Y. (2022). Past, Present, and Future of EEG-Based BCI Applications. Sensors, 22(9), 3331. https://doi.org/10.3390/s22093331
[20]. Douibi, K., Le Bars, S., Lemontey, A., Nag, L., Balp, R., & Breda, G. (2021). Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications. Front. Hum. Neurosci, 15, 705064. https://doi.org/10.3389/fnhum.2021.705064
Cite this article
Liu,Y. (2024). Brain-Computer Interface Application in Recognition and Regulation of Emotion. Theoretical and Natural Science,64,193-200.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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