Palabras clave
Neuroeducación, electroencefalografía, mediciones neurofisiológicas, educación primaria, contexto educativo, estudio de caso
Resumen
Los nuevos dispositivos de electroencefalografía (EEG) inalámbricos permiten realizar registros en contextos fuera del laboratorio. Sin embargo, para su utilización hay que tener en cuenta muchos detalles. En este trabajo, a partir de un estudio de caso instrumental con un grupo de escolares de tercer curso de Educación Primaria, se pretende mostrar algunas potencialidades y limitaciones de la investigación con estos dispositivos en contextos educativos. Se aprecian varios equilibrios en el desarrollo de estas experiencias: entre los intereses y posibilidades de los equipos de investigación y las comunidades educativas; entre la distorsión de la vida en las aulas y las oportunidades de la colaboración entre la academia y la práctica; y entre el presupuesto y la facilidad de preparación de los equipos y la utilidad de los datos recogidos. Entre sus potencialidades encontramos el conocimiento al que permiten acceder sobre diferentes procesos cognitivos y emocionales, y la oportunidad de aprendizaje que suponen los nexos entre investigadores y comunidades educativas. La vida en las aulas se ve interrumpida por este tipo de experiencias, pero ello puede suponer un coste que facilite desarrollos futuros más integrados que beneficien los procesos de enseñanza y aprendizaje.
Referencias
Akalin-Acar, Z., & Makeig, S. (2013). Effects of forward model errors on EEG source localization. Brain topography, 26(3), 378-396. https://doi.org/10.1007/s10548-012-0274-6
Link DOI | Link Google Scholar
Antonenko, P., Paas, F., Grabner, R., & Van-Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22(4), 425-438. https://doi.org/10.1007/s10648-010-9130-y
Link DOI | Link Google Scholar
Basar, E., Basar-Eroglu, C., Karakas, S., & Schürmann, M. (1999). Oscillatory brain theory: A new trend in neuroscience. IEEE engineering in medicine and biology magazine: the quarterly magazine of the Engineering in Medicine & Biology Society, 18(3), 56-66. https://doi.org/10.1109/51.765190
Link DOI | Link Google Scholar
Bevilacqua, D., Davidesco, I., Wan, L., Chaloner, K., Rowland, J., Ding, M., Poeppel, D., & Dikker, S. (2019). Brain-to-brain synchrony and learning outcomes vary by student-teacher dynamics: evidence from a real-world classroom electroencephalography study. Journal of Cognitive Neuroscience, 31(3), 401-11. https://doi.org/10.1162/jocn_a_01274
Link DOI | Link Google Scholar
Browarska, N., Kawala-Sterniuk, A., Zygarlicki, J., Podpora, M., Pelc, M., Martinek, R., & Gorzelanczyk, E.J. (2021). Comparison of smoothing filters' influence on quality of data recorded with the emotiv EPOC Flex brain-computer interface headset during audio stimulation. Brain sciences, 11(1), 98. https://doi.org/10.3390/brainsci11010098
Link DOI | Link Google Scholar
Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32-42. https://doi.org/10.3102/0013189X018001032
Link DOI | Link Google Scholar
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
Link DOI | Link Google Scholar
Craik, A., He, Y., & Contreras-Vidal, J.J. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(3), 031001. https://doi.org/10.1088/1741-2552/ab0ab5
Link DOI | Link Google Scholar
Dikker, S., Haegens, S., Bevilacqua, D., Davidesco, I., Wan, L., Kaggen, L., McClintock, J., Chaloner, K., Ding, M., West, T., & Poeppel, D. (2020). Morning brain: Real-world neural evidence that high school class times matter. Social Cognitive and Affective Neuroscience, 15(11), 1193-1202. https://doi.org/10.1093/scan/nsaa142
Link DOI | Link Google Scholar
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J.J., Ding, M., & Poeppel, D. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology, 27(9), 1375-80. https://doi.org/10.1016/j.cub.2017.04.002
Link DOI | Link Google Scholar
Glaser, B., & Strauss, A. (2006). The discovery of grounded theory. Aldine Transaction.
Link Google Scholar
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
Link DOI | Link Google Scholar
Hajare, R., & Kadam, S. (2021). Comparative study analysis of practical EEG sensors in medical diagnoses. Global Transitions Proceedings, 2(2), 467-475. https://doi.org/10.1016/j.gltp.2021.08.009
Link DOI | Link Google Scholar
Howard-Jones, P.A., Varma, S., Ansari, D., Butterworth, B., De Smedt, B., Goswami, U., Laurillard, D., & Thomas, M.S.C. (2016). The principles and practices of educational neuroscience: Comment on Bowers (2016). Psychological Review, 123(5), 620-627. https://doi.org/10.1037/rev0000036
Link DOI | Link Google Scholar
Janssen, T.W.P., Grammer, J.K., Bleichner, M.G., Bulgarelli, C., Davidesco, I., Dikker, S., Jasi?ska, K.K., Siugzdaite, R., Vassena, E., Vatakis, A., Zion-Golumbic, E., & van Atteveldt, N. (2021). Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience. Mind, Brain and Education, 15(4), 354-370. https://doi.org/10.1111/mbe.12302
Link DOI | Link Google Scholar
Katzir, T., & Paré-Blagoev, J. (2006). Applying cognitive neuroscience research to education: The case of literacy. Educational Psychologist, 41(1), 53-74. https://doi.org/10.1207/s15326985ep4101_6
Link DOI | Link Google Scholar
Khedher, A.B., Jraidi, I., & Frasson, C. (2019). Tracking students’ mental engagement using EEG signals during an interaction with a virtual learning environment. Journal of Intelligent Learning Systems and Applications, 11(1), 1-14. https://doi.org/10.4236/jilsa.2019.111001
Link DOI | Link Google Scholar
Krigolson, O.E., Williams, C.C., Norton, A., Hassall, C.D., & Colino, F.L. (2017). Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research. Frontiers in Neuroscience, 11, 109. https://doi.org/10.3389/fnins.2017.00109
Link DOI | Link Google Scholar
Lau-Zhu, A., Lau, M.P.H., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36, 100635. https://doi.org/10.1016/j.dcn.2019.100635
Link DOI | Link Google Scholar
Liu, Y., & Zhang, Y. (2021). Developing sustaining authentic partnership between MBE researchers and local schools. Mind, Brain, and Education, 15(2), 153-162. https://doi.org/10.1111/mbe.12280
Link DOI | Link Google Scholar
Mason L. (2009). Bridging neuroscience and education: A two-way path is possible. Cortex, 45(4), 548-549. https://doi.org/10.1016/j.cortex.2008.06.003
Link DOI | Link Google Scholar
Matusz, P.J., Dikker, S., Huth, A.G., & Perrodin, C. (2019). Are we ready for real-world neuroscience? Journal of Cognitive Neuroscience, 31(3), 327-338. https://doi.org/10.1162/jocn_e_01276
Link DOI | Link Google Scholar
McMahan, T., Parberry, I., & Parsons, T.D. (2015). Evaluating player task engagement and arousal using electroencephalography. Procedia Manufacturing, 3, 2303-2310. https://doi.org/10.1016/j.promfg.2015.07.376
Link DOI | Link Google Scholar
Pope, A.T., Bogart, E.H., & Bartolome, D.S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1-2), 187-195. https://doi.org/10.1016/0301-0511(95)05116-3
Link DOI | Link Google Scholar
Rose, N., & Abi-Rached, J. (2014). Governing through the brain: Neuropolitics, neuroscience and subjectivity. The Cambridge Journal of Anthropology, 32(1), 3-23. https://doi.org/10.3167/ca.2014.320102
Link DOI | Link Google Scholar
Shad, E.H.T., Molinas, M., & Ytterdal, T. (2020). Impedance and noise of passive and active dry EEG electrodes: a review. IEEE Sensors Journal, 20(24), 14565-14577. https://doi.org/10.1109/JSEN.2020.3012394
Link DOI | Link Google Scholar
Shamay-Tsoory, S.G., & Mendelsohn, A. (2019). Real-life neuroscience: An ecological approach to brain and behavior research. Perspectives on Psychological Science, 14(5), 841-859. https://doi.org/10.1177/1745691619856350
Link DOI | Link Google Scholar
Shkedi, A. (2004). Second?order theoretical analysis: A method for constructing theoretical explanation. International Journal of Qualitative Studies in Education, 17(5), 627-646. https://doi.org/10.1080/0951839042000253630
Link DOI | Link Google Scholar
Stake, R.E. (2010). Qualitative research: Studying how things work. Guilford Publications. https://bit.ly/3J0mmNf
Link Google Scholar
Vekety, B., Logemann, A., & Takacs, Z.K. (2022). Mindfulness practice with a brain-sensing device improved cognitive functioning of elementary school children: An exploratory pilot study. Brain Sciences, 12(1), 103. https://doi.org/10.3390/brainsci12010103
Link DOI | Link Google Scholar
Williams, N.S., McArthur, G.M., & Badcock, N.A. (2020a). 10 years of EPOC: A scoping review of Emotiv’s portable EEG device. BioRxiv. https://doi.org/10.1101/2020.07.14.202085
Link DOI | Link Google Scholar
Williams, N.S., McArthur, G.M., de-Wit, B., Ibrahim, G., & Badcock, N.A. (2020b). A validation of Emotiv EPOC Flex saline for EEG and ERP research. PeerJ, 8, e9713. https://doi.org/10.7717/peerj.9713
Link DOI | Link Google Scholar
Williamson, B. (2018). Brain data: Scanning, scraping and sculpting the plastic learning brain through neurotechnology. Postdigital Science and Education, 1, 65-86. https://doi.org/10.1007/s42438-018-0008-5
Link DOI | Link Google Scholar
Xu, J., & Zhong, B. (2018). Review on portable EEG technology in educational research. Computers in Human Behavior, 81, 340-349. https://doi.org/10.1111/mbe.12314
Link DOI | Link Google Scholar
Xu, K., Torgrimson, S.J., Torres, R., Lenartowicz, A., & Grammer, J.K. (2022). EEG data quality in real?world settings: Examining neural correlates of attention in school?aged children. Mind, Brain, and Education, 16(3), 221-227. https://doi.org.ponton.uva.es/10.1111/mbe.12314
Link DOI | Link Google Scholar
Zerafa, R., Camilleri, T., Falzon, O., & Camilleri, K.P. (2018). A comparison of a broad range of EEG acquisition devices– is there any difference for SSVEP BCIs? Brain-Computer Interfaces, 5(4), 121-131 https://doi.org/10.1080/2326263X.2018.1550710
Link DOI | Link Google Scholar
Ficha técnica
Recibido: 28-12-2022
Revisado: 18-01-2023
Aceptado: 23-02-2023
OnlineFirst: 30-05-2023
Fecha publicación: 01-07-2023
Tiempo de revisión del artículo : 21 (en días) | Media de tiempo de revisión de los manuscritos del número 76: -6 (en días)
Tiempo de aceptación del artículo: 57 (en días) | Media tiempo aceptación de los manuscritos del número 76: 72 (en días)
Tiempo de edición OnlineFirst: 140 (en días) | Media tiempo edición de los OnlineFirst del número 76: 155 (en días)
Tiempo de publicacicón final del artículo: 185 (en días) | Media tiempo de publicación final de los articulos del número 76: 200 (en días)
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Alfonso García-Monge, Universidad de Valladolid (España)
