Electroencephalographic activity associated with the relationship cognitive load, stress and emotion recognition in a focus group of university professors of experimental sciences
DOI:
https://doi.org/10.1344/joned.v5i2.46837Keywords:
Cognitive load, Electroencephalography, Science education, Stress, Executive functions, Emotion recognitionAbstract
This article presents the recording and analysis of the electrical activity of EEG signals in situations of cognitive load, stress and emotion recognition as part of a doctoral research that was carried out with a focus group of Colombian university professors of experimental sciences. One of the purposes of the study was to analyze the physiological changes in the electrical activity of the brain due to teaching activities that involve cognitive load, stress and the recognition of emotions that triggers this process for the group of teachers. To acquire the rhythms of the EEG signals, the Emotiv EPOC+ device and a group of psychometric tests adapted to induce signals in relation to tasks associated with teaching, such as attention and memory, were used. Spectral power density was used as a mathematical function and the signals were used with PSD, permutation entropy and approximate entropy and classification was performed with k-nearest neighbors. It is concluded that there was a predominance of theta-alpha rhythms in the majority of the teachers in the study, in addition, activities of attention, working memory and other executive functions were identified with a performance of 75.1 ± 3.05% with the classifier and, finally, They recognize mixed emotions, some of which are predictors of work stress, such as anxiety and tension, which affect teaching practice and professional development.
References
Chiang, H. S., Chen, M. Y., & Liao, L. S. (2022). Cognitive depression detection cyber-medical system based on EEG analysis and deep learning approaches. IEEE Journal of Biomedical and Health Informatics, 27(2), 608-616. https://doi.org/10.1109/JBHI.2022.3200522
Matusz, P. J., Turoman, N., Tivadar, R. I., Retsa, C., & Murray, M. M. (2019). Brain and cognitive mechanisms of top–down attentional control in a multisensory world: Benefits of electrical neuroimaging. Journal of cognitive neuroscience, 31(3), 412-430. https://doi.org/10.1162/jocn_a_01360
Yen, C.; Lin, C.-L.; Chiang, M.-C. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life 2023, 13, 1472. https://doi.org/10.3390/life13071472
Bruya, B., & Tang, Y. Y. (2018). Is attention really effort? Revisiting Daniel Kahneman’s influential 1973 book attention and effort. Frontiers in psychology, 9, 372633. https://doi.org/10.3389/fpsyg.2018.01133
Adolphs, R., Tranel, D., & Damasio, A. R. (2013). The human amygdala in social judgment. In Autism (pp. 136-140). Routledge.
Koelstra, S., Muhl, C., Soleymani, M., Lee, J. S., Yazdani, A., Ebrahimi, T., ... & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE transactions on affective computing, 3(1), 18-31.
Shulman, L. S. (2015). PCK: Su génesis y éxodo. En Reexaminando el conocimiento pedagógico del contenido en la enseñanza de las ciencias (pp. 3-13). Routledge.
Sutton, R. E., & Wheatley, K. F. (2003). Teachers’ emotions and teaching: A review of the literature and directions for future research. Educational psychology review, 15, 327-358. https://doi.org/10.1023/A:1026131715856
Zembylas, M. (2003). Emotions and teacher identity: A poststructural perspective. Teachers and teaching, 9(3), 213-238. https://doi.org/10.1080/13540600309378
Sinatra, Gale M., Suzanne H. Broughton, and D. O. U. G. Lombardi. “Emotions in science education.” International handbook of emotions in education. Routledge, 2014. 415-436.
Goswami, U. (2004). Neuroscience and Education. British Journal of Educational Psychology, 74, 1–14. https//doi.org/10.1348/000709904322848798
Blakemore S. J. y Frith U. (2005). The learning brain: Lessons for education: A precis. Developmental Science, 8, 459–465. https://doi.org/10.1111/j.1467-7687.2005.00434.x
Ansari, D. y Coch, D. (2006). Bridges Over Troubled Waters: Education and cognitive neuroscience. Trends in Cognitive Sciences, 10(4), 146–151. https://doi.org/10.1016/j.tics.2006.02.007
Abell, S. K. (2008). Twenty years later: Does pedagogical content knowledge remain a useful idea? International journal of science education, 30(10), 1405-1416. https://doi.org/10.1080/09500690802187041
Van Driel, J. H., Berry, A., & Meirink, J. (2014). Research on science teacher knowledge. In Handbook of research on science education, volume II (pp. 862-884). Routledge.
Litwińczuk, M. C., Muhlert, N., Trujillo‐Barreto, N., & Woollams, A. (2023). Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance. Human Brain Mapping, 44(8), 3007-3022. https://doi.org/10.1002/hbm.26258
Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K., & Carlén, M. (2016). Prefrontal parvalbumin neurons in control of attention. Cell, 164(1), 208-218. http://dx.doi.org/10.1016/j.cell.2015.11.038
Yen, C.; Lin, C.-L.; Chiang, M.-C. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life 2023, 13, 1472. https://doi.org/10.3390/life13071472
Muñoz-Gutiérrez, P. A., Giraldo, E., Bueno-López, M., & Molinas, M. (2018). Localization of active brain sources from EEG signals using empirical mode decomposition: a comparative study. Frontiers in integrative neuroscience, 12, 55. https://doi.org/10.3389/fnint.2018.00055
Pfurtscheller, G., Stancak Jr, A., & Neuper, C. (1996). Event-related synchronization (ERS) in the alpha band—an electrophysiological correlate of cortical idling: a review. International journal of psychophysiology, 24(1-2), 39-46. https://doi.org/10.1016/S0167-8760(96)00066-9.
Harmony, T., Fernández, T., Silva, J., Bernal, J., Díaz-Comas, L., Reyes, A., ... & Rodríguez, M. (1996). EEG delta activity: an indicator of attention to internal processing during performance of mental tasks. International journal of psychophysiology, 24(1-2), 161-171. https://doi.org/10.1016/S0167-8760(96)00053-0
Ko, L. W., Komarov, O., Hairston, W. D., Jung, T. P., & Lin, C. T. (2017). Sustained attention in real classroom settings: An EEG study. Frontiers in human neuroscience, 11, 388. https://doi.org/10.3389/fnhum.2017.00388
Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human Behavior, 81, 340-349. https://doi.org/10.1016/j.chb.2017.12.03768
Grammer, J. K., Xu, K., & Lenartowicz, A. (2021). Effects of context on the neural correlates of attention in a college classroom. NPJ science of learning, 6(1), 15. https://doi.org/10.1038/s41539-021-00094-8
Dong, J. (2022). Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis. Occupational Therapy International, 2022. https://doi.org/10.1155/2022/2593338
Díaz, D., Hernández, E., & Rolo, G. (2012). Carga mental de trabajo. España: Ed. Síntesis.
Najmi, S., Amir, N., Frosio, K. E., & Ayers, C. (2015). The effects of cognitive load on attention control in subclinical anxiety and generalised anxiety disorder. Cognition and Emotion, 29(7), 1210-1223. https://doi.org/10.1080/02699931.2014.975188
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational psychologist, 38(1), 1-4. https://doi.org/10.1207/S15326985EP3801_1
Pacheco-Unguetti, A., Acosta, A., Lupiáñez, J., Román, N., & Derakshan, N. (2012). Response inhibition and attentional control in anxiety. Quarterly Journal of Experimental Psychology, 65(4), 646-660. https://doi.org/10.1080/17470218.2011.637114
Mejías, S. S., Martinez, K., Centeno, F., & Sellas, C. (2022). Cognitive Load and Attentional Control in Puerto Ricans with Obsessive-Compulsive Disorder. Revista Iberoamericana de Psicología: Ciencia y Tecnología, 15(1), 21-34. https://doi.org/10.33881/2027-1786.rip.15103
Gevins, A., & Smith, M. E. (2003). Neurophysiological measures of cognitive workload during human-computer interaction. Theoretical issues in ergonomics science, 4(1-2), 113-131. https://doi.org/10.1080/14639220210159717
Jensen, O., & Tesche, C. D. (2002). Frontal theta activity in humans increases with memory load in a working memory task. European journal of Neuroscience, 15(8), 1395-1399. https://doi.org/10.1046/j.1460-9568.2002.01975.x
Sterman, M. B., Mann, C. A., Kaiser, D. A., & Suyenobu, B. Y. (1994). Multiband topographic EEG analysis of a simulated visuomotor aviation task. International journal of psychophysiology, 16(1), 49-56. https://doi.org/10.1016/0167-8760(94)90041-8
Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., & Babiloni, F. (2014). Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neuroscience & Biobehavioral Reviews, 44, 58-75. https://doi.org/10.1016/j.neubiorev.2012.10.003
Cacioppo, J. T., Berntson, G. G., Sheridan, J. F., & McClintock, M. K. (2000). Multilevel integrative analyses of human behavior: social neuroscience and the complementing nature of social and biological approaches. Psychological bulletin, 126(6), 829.
Keltner, D., Ekman, P., Gonzaga, G. C., & Beer, J. (2000). Facial expression of emotion. New York: Guilford Publications, 236-248.
Carmona-Fuentes, P., & Rosas-Reyes, R. (2017). Influencia de la inteligencia emocional en el desempeño laboral. Revista Líder, 19(30), 107-118. Recuperado a partir de https://revistaliderchile.ulagos.cl/index.php/liderchile/article/view/2425
Ekman, P. (2016). What scientists who study emotion agree about. Perspectives on psychological science, 11(1), 31-34. https://doi.org/10.1177/1745691615596992
Coan, J. A., & Allen, J. J. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Biological psychology, 67(1-2), 7-50. https://doi.org/10.1016/j.biopsycho.2004.03.002
Nie, D., Wang, X. W., Shi, L. C., & Lu, B. L. (2011, April). EEG-based emotion recognition during watching movies. In 2011 5th international IEEE/EMBS conference on neural engineering (pp. 667-670). IEEE. https://doi.org/10.1109/NER.2011.5910636.
Da Silva, N. R., & Pinheiro, D. M. (2017). Agruras no trabalho do professor/Troubles in the teacher’s work. Cadernos Brasileiros de Terapia Ocupacional, 25(4), 713-721. https://doi.org/10.4322/2526-8910.ctoAO0928
Iqbal, M. U., Srinivasan, B., & Srinivasan, R. (2020). Dynamic assessment of control room operator’s cognitive workload using Electroencephalography (EEG). Computers & Chemical Engineering, 141, 106726. https://doi.org/10.1016/j.compchemeng.2020.106726
Najmi, S., Amir, N., Frosio, K. E., & Ayers, C. (2015). The effects of cognitive load on attention control in subclinical anxiety and generalised anxiety disorder. Cognition and Emotion, 29(7), 1210-1223. https://doi.org/10.1080/02699931.2014.975188
Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition & emotion, 6(6), 409-434. https://doi.org/10.1080/02699939208409696
Lavie, N. (2000). Selective attention and cognitive control: Dissociating attentional functions through different types of load. MIT press.
Creswell, J.W., & Clark, V.L.P. (2011). Designing and conducting mixed methods research (2e ed.). Sage Publications.
Latorre, A., & Del Rincón, D. A. J. (2003): Bases Metodológicas de la Investigación Educativa. Ediciones Experiencia, Barcelona.
Stake, R. E. (2005). Qualitative case studies.
Echeverry, J. D., Lemus, C. G., & Orozco, Á. Á. (2007) Análisis de la densidad espectral de potencia en registros mer. Scientia et technica, 1(35). https://doi.org/10.22517/23447214.5379.
Mulla, F., Eya, E., Ibrahim, E., Alhaddad, A., Qahwaji, R., & Abd-Alhameed, R. (2017, September). Neurological assessment of music therapy on the brain using Emotiv Epoc. In 2017 Internet Technologies and Applications (ITA) (pp. 259-263). IEEE. https://doi.org/10.1109/ITECHA.2017.8101950
Boonyakitanont, P., Lek-Uthai, A., Chomtho, K., & Songsiri, J. (2020). A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomedical Signal Processing and Control, 57, 101702. https://doi.org/10.1016/j.bspc.2019.101702
Balcik, Z. E., Senadim, S., Tekin, B., Dirican, A. C., Eren, F., Karahan, M. G., ... & Atakli, D. (2020). Do interictal EEG findings reflect cognitive function in juvenile myoclonic epilepsy? Epilepsy & Behavior, 111, 107281. https://doi.org/10.1016/j.yebeh.2020.107281
Sánchez-Díaz, G., Escobar-Franco, U. E., Morales-Manilla, L. R., Piza-Dávila, I., Aguirre-Salado, C., & Franco-Arcega, A. (2013). Un algoritmo de clasificación incremental basado en los k vecinos más similares para datos mezclados. Revista Facultad de Ingeniería Universidad de Antioquia, (67), 19-30.
Ostrosky-Solís, F., Ardila, A., & Roselli, M. (1999). Neuropsi: A brief neuropsychological test battery in Spanish with norms by age and educational level. Journal of International Neuropsychological Society, 5, 413–433.
Heaton, R. K., & Staff, P. A. R. (1993). Wisconsin card sorting test: computer version 2. Odessa: Psychological Assessment Resources, 4, 1-4.
Schomer, Donald L., and Fernando Lopes Da Silva. Niedermeyer’s electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins, 2012.
Britton, J. W., Frey, L. C., Hopp, J. L., Korb, P., Koubeissi, M. Z., Lievens, W. E., ... & St Louis, E. K. (2016). Electroencephalography (EEG): An introductory text and atlas of normal and abnormal findings in adults, children, and infants.
Sanei, S., & Chambers, J. A. (2013). EEG signal processing. John Wiley & Sons.
Stuss, D. T., Benson, D. F., Clermont, R., Della Malva, C. L., Kaplan, E. F., & Weir, W. S. (1986). Language functioning after bilateral prefrontal leukotomy. Brain and language, 28(1), 66-70. https://doi.org/10.1016/0093-934X(86)90091-X
Goldman-Rakic, P. S. (1987). Development of cortical circuitry and cognitive function. Child development, 601-622. https://doi.org/10.2307/1130201
Fuster, J. M., Bauer, R. H., & Jervey, J. P. (1985). Functional interactions between inferotemporal and prefrontal cortex in a cognitive task. Brain research, 330(2), 299-307. https://doi.org/10.1016/0006-8993(85)90689-4
Petrides, M. (1991). Functional specialization within the dorsolateral frontal cortex for serial order memory. Proceedings of the Royal Society of London. Series B: Biological Sciences, 246(1317), 299-306. https://doi.org/10.1098/rspb.1991.0158
Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 351(1346), 1413-1420. https://doi.org/10.1098/rstb.1996.0125
Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4-5), 287-305. https://doi.org/10.1016/j.neucli.2016.07.002
Solís-Ortiz, S., & Gutiérrez, M. (2011). Correlación entre los puntajes del Wisconsin Card Sorting Test y la actividad electroencefalográfica en mujeres en la posmenopausia. Revista Chilena de Neuropsicología, 6(2), 76-79.
Fairclough, S. H., & Roberts, J. S. (2011). Effects of performance feedback on cardiovascular reactivity and frontal EEG asymmetry. International Journal of Psychophysiology, 81(3), 291-298. https://doi.org/10.1016/j.ijpsycho.2011.07.012
Doppelmayr, M., Finkenzeller, T., & Sauseng, P. (2008). Frontal midline theta in the pre-shot phase of rifle shooting: differences between experts and novices. Neuropsychologia, 46(5), 1463-1467. https://doi.org/10.1016/j.neuropsychologia.2007.12.026
Konar, A., Chakraborty, A., Bhowmik, P., Das, S., & Halder, A. (2012). Emotion recognition from facial expression and electroencephalogram signals. In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition: Advancing Technologies (pp. 310-337). IGI Global. https://doi.org/10.4018/978-1-61350-429-1.ch017
Chai, M., & Ba, L. (2021). Application of EEG signal recognition method based on duffing equation in psychological stress analysis. Advances in Mathematical Physics, 2021, 1-10. https://doi.org/10.1155/2021/1454547
Brooks, J. A., Chikazoe, J., Sadato, N., & Freeman, J. B. (2019). The neural representation of facial-emotion categories reflects conceptual structure. Proceedings of the National Academy of Sciences, 116(32), 15861-15870. https://doi.org/10.1073/pnas.1816408116
Mayer, John D., Peter Salovey, and David Caruso. “Models of emotional intelligence.” Handbook of intelligence 2 (2000): 396-420.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Carolina María González Velásquez, Bartolomé Vázquez Bernal, María Ángeles De las Heras Pérez, Johnatan Alexander Mena Salcedo, Mateo Osorio Higuita, Juan Pablo Murillo Escobar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors who publish in this journal agree to the following terms:
a. Authors retain copyright and grant the journal the right of first publication
b. Texts will be published under a Creative Commons Attribution Non Commercial License that allows others to share the work, provided they include an acknowledgement of the work’s authorship, its initial publication in this journal and the terms of the license, and not for commercial use.
