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Revista ELECTRO

Vol. 47 – Año 2025

Artículo

TÍTULO

Análisis de Habilidades de Cognición Social para Evaluar el Impacto de Intervenciones Psicológicas Mediante Procesamiento Digital de Señales EEG

AUTORES

Esqueda, J.J.; Roa-Rico D.O.; Galindo-Aldana, G.M.; Trigueros-Triana, L.G.; Mora-Navarro, A.L.; Castillo-Álvarez, Y.; Zamora-Rochín, L.; Torres-González, C.; Montoya-Rivera, L.A.; Inzunza-González, E.; Jiménez-Beristáin, L.; Luengas-Bernal, K.V.; Oliveros-Martínez, E.J.

RESUMEN

Se presentan resultados parciales de un proyecto de investigación sobre el impacto de intervenciones psicológicas en alumnos universitarios con síntomas internalizantes (estrés y ansiedad). Alumnos universitarios de la UABC fueron previamente evaluados por un equipo de psicología para posteriormente participar en diez sesiones de intervenciones psicológicas semanales, impartidas por alumnos de psicología. Se toma registro electroencefalográfico de cinco minutos en reposo con los ojos cerrados a cada partici pante con la interfaz Cerebro-Computadora EPOC X antes de la primera intervención y al final. Asimismo, se les aplica el Reading the Mind in the Eyes Test (RMET, Test de la Mirada) antes de la primera intervención y al final de la última. Se realiza un com parativo de las potencias de las bandas Alfa, Beta Baja y Beta Alta previas y postintervenciones, así como con los resultados del RMET.

Palabras Clave: EEG, Cognición Social, Intervención Clínica, Test de la Mirada, Síntomas Internalizantes.

ABSTRACT

Partial results of a research project on the impact of psychological interventions on university students with internalizing symptoms (stress and anxiety) are presented. University students from the UABC were previously evaluated by a psychology team to la ter participate in ten weekly psychological intervention sessions, taught by psychology students. A five-minute electroencephalographic recording is taken at rest with closed eyes from each participant with the EPOC X Brain-Computer interface before the fi rst intervention and at the end. Likewise, the Reading the Mind in the Eyes Test (RMET) is applied before the first intervention and at the end of the last one. A comparison of the powers of the Alpha, Low Beta, and High Beta bands is made before and after interventions, as well as in relation to the results of the RMET.

Keywords: EEG, Social Cognition, Clinical Intervention, Gaze Test, Internalizing Symptoms.

REFERENCIAS

[1] C. Beaudoin and M. H. Beauchamp, “Social cognition,” Handb Clin Neurol, vol. 173, pp. 255 –264, Jan. 2020, doi: 10.1016/B978-0-444-64150-2.00022-8.
[2] M. Loubat, A. Astudillo-Zúñiga, M. Loubat, and A. Astudillo-Zúñiga, “Revisión sistemática (2010-2017) de los instrumentos de evaluación y programas de rehabilitación de la cognición social utilizados con persona s con esquizofrenia,” Terapia psicológica, vol. 37, no. 3, pp. 295 –316, Dec. 2019, doi: 10.4067/S0718-48082019000300295.
[3] F. Deng et al., “Assessing social cognition in patients with schizophrenia and healthy controls using the reading the mind in the e yes test (RMET): a systematic review and meta-regression,” Psychol Med, vol. 54, no. 5, pp. 847 –873, Apr. 2024, doi: 10.1017/S0033291723003501.
[4] D. I. Tsomokos and E. Flouri, “The role of social cognition in mental health trajectories from childhood to adolescence,” Eur Child Adolesc Psychiatry, vol. 33, no. 3, pp. 771–786, Mar. 2024, doi: 10.1007/S00787-023-02187-8/METRICS.
[5] J. B. Klein, R. H. Jacobs, and M. A. Reinecke, “Cognitive-Behavioral Therapy for Adolescent Depression: A Meta-Analytic Investi gation of Changes in Effect-Size Estimates,” J Am Acad Child Adolesc Psychiatry, vol. 46, no. 11, pp. 1403 –1413, Nov. 2007, doi: 10.1097/CHI.0B013E3180592AAA.
[6] M. F. D. S. Tursi, C. V. W. Baes, F. R. D. B. Camacho, S. M. D. C. Tofoli, and M. F. Juruena, “Effectiveness of psychoeducation for depression: A systematic review,” Australian and New Zealand Journal of Psychiatry, vol. 47, no. 11, pp. 1019 –1031, Nov. 2013, doi: 10.1177/0004867413491154/FORMAT/EPUB.
[7] M. T. Miró et al., “Eficacia de los tratami entos psicológicos basados en mindfulness para los trastornos de ansiedad y depresión : una revisión sistemática,” Revista de Psicopatología y Psicología Clínica, vol. 16, no. 1, pp. 1 –16, Apr. 2011, doi: 10.5944/RPPC.VOL.16.NUM.1.2011.10347.
[8] Y.-Q. Gu and Y. Zhu, “Underlying mechanisms of mindfulness meditation: Genomics, circuits, and networks,” http://www.wjgnet.com/, vol. 12, no. 9, pp. 1141 –1149, Sep. 2022, doi: 10.5498/WJP.V12.I9.1141.
[9] E. de Jonge et al., “Atypical Resting-State EEG Graph Metri cs of Network Efficiency Across Development in Autism and Their Association with Social Cognition: Results from the LEAP Study,” J Autism Dev Disord, vol. 9, pp. 1 –17, Feb. 2025, doi: 10.1007/S10803-025-06731-0/FIGURES/3.
[10] S. Galluzzi et al., “Cognitiv e, psychological, and physiological effects of a web-based mindfulness intervention in older adults during the COVID-19 pandemic: an open study,” BMC Geriatr, vol. 24, no. 1, pp. 1 –16, Dec. 2024, doi: 10.1186/S12877-024-04766-Z/FIGURES/4.
[11] M. Sanada an d Y. Naruse, “EEG synchronisation reveals the impact of group identity and membership duration on social cognitive bias,” Sci Rep, vol. 15, no. 1, pp. 1 –13, Dec. 2025, doi: 10.1038/S41598-025-08191-Z;SUBJMETA.
[12] E. S. Norton et al., “Social EEG: A novel neurodevelopmental approach to studying brain-behavior links and brain-to-brain synchrony during naturalistic toddler –parent interactions,” Dev Psychobiol, vol. 64, no. 3, Mar. 2022, doi: 10.1002/dev.22240.
[13] Y. Grootjans et al., “Getting closer to soc ial interactions using electroencephalography in developmental cognitive neuroscience,” Dev Cogn Neurosci, vol. 67, Jun. 2024, doi: 10.1016/j.dcn.2024.101391.
[14] G. Dawson et al., “Early Behavio ral Intervention Is Associated with Normalized Brain Activit y in Young Children With Autism,” J Am Acad Child Adolesc Psychiatry, vol. 51, no. 11, pp. 1150 –1159, 2012, doi: 10.1016/j.jaac.
[15] Y. Yamada et al., “Social Cognition Deficits as a Target of Early Intervention for Psychoses: A Systematic Review,” Front Psychiatry, vol. 10, no. MAY, 2019, doi: 10.3389/FPSYT.2019.00333.
[16] E. Gkintoni, A. Aroutzidis, H. Antonopoulou, and C. Halkiopoulos, “From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neurosc ience and Real-World Applications,” Brain Sci, vol. 15, no. 3, p. 220, Mar. 2025, doi: 10.3390/BRAINSCI15030220/S1.
[17] A. Chaddad, Y. Wu, R. Kateb, and A. Bouridane, “Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Method s and Techniques,” Jul. 01, 2023, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s23146434.
[18] F. Caiado and A. Ukolov, “The history, current state and future possibilities of the non-invasive brain computer interfaces,” Med Nov Tech nol Devices, vol. 25, p. 100353, Mar. 2025, doi: 10.1016/J.MEDNTD.2025.100353.
[19] A. W. Chan et al., “SPIRIT 2013 statement: Defining standard protocol items for clinical trials,” Ann Intern Med, vol. 158, no. 3, pp. 200 –207, Feb. 2013, doi: 10.7326/0003-4819-158-3-201302050-00583/ASSET/IMAGES/10TT2.JPG.
[20] Emotiv, “How are band powers calculated?” Accessed: Jul. 09, 2025. [Online]. Available: https://www.emotiv.com/tools/knowledge-base/technical-research-information/how-are-band-powers-calculate d

CITAR COMO:

Esqueda, J.J.; Roa-Rico D.O.; Galindo-Aldana, G.M.; Trigueros-Triana, L.G.; Mora-Navarro, A.L.; Castillo-Álvarez, Y.; Zamora-Rochín, L.; Torres-González, C.; Montoya-Rivera, L.A.; Inzunza-González, E.; Jiménez-Beristáin, L.; Luengas-Bernal, K.V.; Oliveros-Martínez, E.J., "Análisis de Habilidades de Cognición Social para Evaluar el Impacto de Intervenciones Psicológicas Mediante Procesamiento Digital de Señales EEG", Revista ELECTRO, Vol. 47, 2025, pp. 308-313.

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