Analysis of emotional indicators of human talent in a company’s service sales area using a brain-computer interface

Authors

DOI:

https://doi.org/10.61799/2216-0388.2363

Keywords:

Brain-computer interface, emotional indicators, human talent, neuroergonomics, sales

Abstract

This article presents an exploratory pilot study aimed at analyzing emotional responses during a sales process within a company. A brain–computer interface was used to estimate, in real time and under real-world working conditions, the salesperson’s emotional indicators of stress, excitement, engagement, and interest, as defined by the manufacturer’s proprietary performance metrics. Two service sales processes involving low and high levels of complexity were evaluated, and the salesperson’s emotional responses were compared with those observed during the baseline condition and during routine administrative tasks unrelated to sales activities. The study showed a high level of dedication to work, as reflected in the engagement and interest indicators. Additionally, the stress and excitement indicators estimated through the brain–computer interface showed the greatest variations associated with changes in task complexity, particularly the latter neurophysiological indicator.

Downloads

Download data is not yet available.

References

[1] Y. N. Cardona-Álvarez, A. M. Álvarez-Meza, D. A. Cárdenas-Peña, G. A. Castaño-Duque, and G. Castellanos-Dominguez, “A Novel OpenBCI Framework for EEG-Based Neurophysiological Experiments,” Sensors, vol. 23, no. 7, Apr. 2023, doi: 10.3390/s23073763.

[2] D. Wu, Y. Xu, and B.-L. Lu, “Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016,” IEEE Trans. Cogn. Dev. Syst., vol. 8920, no. c, pp. 1–1, 2020, doi: 10.1109/tcds.2020.3007453.

[3] H. A. Hamzah and K. K. Abdalla, “EEG-based emotion recognition systems; comprehensive study,” May 30, 2024, Elsevier Ltd. doi: 10.1016/j.heliyon.2024.e31485.

[4] C. A. Peña-Cortés, A. L. Vargas-Granados, and A. Pardo-García, “Análisis del nivel de compromiso en la programación de dispositivos hápticos por medio de una interfaz cerebro computador,” Revista de Investigación, Desarrollo e Innovación, vol. 12, no. 1, pp. 137–148, Feb. 2022, doi: 10.19053/20278306.v12.n1.2022.14214.

[5] Y. S. Can, N. Chalabianloo, D. Ekiz, J. Fernandez-Alvarez, G. Riva, and C. Ersoy, “Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection with Smartwatches,” IEEE Access, vol. 8, pp. 38146–38163, 2020, doi: 10.1109/ACCESS.2020.2975351.

[6] T. Jiang, J. Wu, and S. C. H. Leung, “The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis,” Front. Comput. Neurosci., vol. 19, 2025, doi: 10.3389/fncom.2025.1556483.

[7] N. S. Suhaimi, J. Mountstephens, and J. Teo, “EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities,” 2020, Hindawi Limited. doi: 10.1155/2020/8875426.

[8] L. Moreno, C. Peña, and H. González, “Integración de un sistema de neuroseñales para detectar expresiones en el análisis de material multimedia,” Revista Facultad de Ingeniería, vol. 24, no. 38, pp. 29–40, 2014, [Online]. Available: http://revistas.uptc.edu.co/index.php/ingenieria/article/view/3156/4343

[9] A. Costa-Feito, A. M. González-Fernández, C. Rodríguez-Santos, and M. Cervantes-Blanco, “Electroencephalography in consumer behaviour and marketing: a science mapping approach,” Dec. 01, 2023, Springer Nature. doi: 10.1057/s41599-023-01991-6.

[10] M. C. Tsai, “Storytelling advertising investment profits in marketing: From the perspective of consumers’ purchase intention,” Mathematics, vol. 8, no. 10, pp. 1–12, Oct. 2020, doi: 10.3390/math8101704.

[11] A. H. Alsharif, N. Z. M. Salleh, M. Abdullah, A. Khraiwish, and A. Ashaari, “Neuromarketing Tools Used in the Marketing Mix: A Systematic Literature and Future Research Agenda,” Sage Open, vol. 13, no. 1, Jan. 2023, doi: 10.1177/21582440231156563.

[12] C. Peña, S. Caicedo, L. Moreno, M. Maestre, and A. Pardo, “Use of a Low Cost Neurosignals Capture System to Show the Importance of Developing Didactic Activities Within a Class to Increase the Level of Student Engagement. ( Case Study ),” WSEAS Transaction on Computers, vol. 16, pp. 172–178, 2017, [Online]. Available: http://www.wseas.org/multimedia/journals/computers/2017/a385905-070.php

[13] L. Moreno, C. Peña, M. Maestre, S. Caicedo, and A. Pardo, “Registro de Neuroseñales con una Interfaz Cerebro-Computador para Estimar el Nivel Estrés en un Estudiante durante una Clase,” INGE-CUC, vol. 13, no. 2, pp. 95–101, 2017, doi: http://doi.org/10.17981/ingecuc.13.2.2017.10.

[14] L. Moreno, C. Peña, and S. Patiño, “Estimación de los niveles de entusiasmo de un cliente al escuchar una propuesta de un proyecto empleando diversos medios tecnológicos.,” in VII Congreso Internacional de Ciencias Económicas y Administrativas Horizontes empresariales: redefiniendo los modelos de negocio, San Gil: Fundación Universitaria San Gil, Oct. 2025. [Online]. Available: https://unisangil.edu.co/

[15] C. Paez, C. Peña, and A. Pardo, “Estudio de la influencia de la actividad física sobre la calidad del sueño y los niveles de atención utilizando herramientas tecnológicas,” Mundo FESC, vol. 11, no. 4s, pp. 310–323, 2021, Accessed: Jan. 29, 2026. [Online]. Available: https://doi.org/10.61799/2216-0388.1110

[16] M. Aldayel, M. Ykhlef, and A. Al-Nafjan, “Recognition of Consumer Preference by Analysis and Classification EEG Signals,” Front. Hum. Neurosci., vol. 14, Jan. 2021, doi: 10.3389/fnhum.2020.604639.

[17] F. R. Mashrur et al., “BCI-Based Consumers’ Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework,” Front. Hum. Neurosci., vol. 16, May 2022, doi: 10.3389/fnhum.2022.861270.

[18] D. Caruelle, P. Shams, A. Gustafsson, and L. Lervik-Olsen, “Emotional arousal in customer experience: A dynamic view,” J. Bus. Res., vol. 170, Jan. 2024, doi: 10.1016/j.jbusres.2023.114344.

[19] H. Park, H. Oh, and S. Boo, “The role of occupational stress in the association between emotional labor and mental health: A moderated mediation model,” Sustainability (Switzerland), vol. 11, no. 7, Apr. 2019, doi: 10.3390/su11071886.

[20] Y. Wang, Y. Huang, B. Gu, S. Cao, and D. Fang, “Identifying mental fatigue of construction workers using EEG and deep learning,” Autom. Constr., vol. 151, Jul. 2023, doi: 10.1016/j.autcon.2023.104887.

[21] M. Płaza et al., “Emotion Recognition Method for Call/Contact Centre Systems,” Applied Sciences (Switzerland), vol. 12, no. 21, Nov. 2022, doi: 10.3390/app122110951.

[22] A. Terim, S. N. Çağlayan, A. Kıvılcım, and M. Aktaş, “Business Process Architecture for Sentiment Analysis on Speech Data,” Orclever Proceedings of Research and Development, vol. 1, no. 1, pp. 307–320, Dec. 2022, doi: 10.56038/oprd.v1i1.211.

[23] S. Fikri, P. Kusumawardhani, E. D. Pratiwi, and A. Adyatama, “Occupational stress measurement using Electroencephalogram: Corporate customer care agent in telecommunication company case study,” in 2017 6th International Conference on Industrial Technology and Management (ICITM), 2017, pp. 60–63. doi: 10.1109/ICITM.2017.7917896.

[24] P. Liu, Y. Zhang, Z. Xiong, Y. Wang, and L. Qing, “Judging the emotional states of customer service staff in the workplace: A multimodal dataset analysis,” Front. Psychol., vol. 13, Nov. 2022, doi: 10.3389/fpsyg.2022.1001885.

[25] J. Sabio, N. S. Williams, G. M. McArthur, and N. A. Badcock, “A scoping review on the use of consumer-grade EEG devices for research,” PLoS One, vol. 19, no. 3 March, Mar. 2024, doi: 10.1371/journal.pone.0291186.

[26] C. Rico-Olarte, B. M. Eskofier, and D. M. Lopez, “EEG-cleanse: an automated pipeline for cleaning electroencephalography recordings during full-body movement,” MethodsX, vol. 15, Dec. 2025, doi: 10.1016/j.mex.2025.103702.

[27] M. Galigani, N. Castellani, B. Italia, S. D’Aversa, D. Bottari, and F. Garbarini, “Frequency-tagging EEG reveals spontaneous categorical discrimination of visual self-identity,” iScience, vol. 28, no. 10, Oct. 2025, doi: 10.1016/j.isci.2025.113562.

[28] M. Zabcikova, Z. Koudelkova, and R. Jasek, “Examining the Efficiency of Emotiv Insight Headset by Measuring Different Stimuli,” WSEAS Transactions on Applied and Theoretical Mechanics, vol. 14, pp. 235–242, 2019, [Online]. Available: https://wseas.com/journals/articles.php?id=1869

[29] J. A. Corredor Camargo, C. A. Peña Cortés, and A. Pardo García, “Evaluación de las emociones de usuarios en tareas con realimentación háptica utilizado el dispositivo Emotiv Insight,” INGE CUC, vol. 15, no. 1, pp. 9–16, Mar. 2019, doi: 10.17981/ingecuc.15.1.2019.01.

Published

2027-01-01

Issue

Section

Artículo Originales

How to Cite

[1]
Moreno Cueva, L.A. et al. 2027. Analysis of emotional indicators of human talent in a company’s service sales area using a brain-computer interface. Mundo FESC Journal. 17, 37 (Jan. 2027). DOI:https://doi.org/10.61799/2216-0388.2363.

Most read articles by the same author(s)