Potencialidades y limitaciones de la usabilidad de dispositivos EEG en contextos educativos

  1. Alfonso García-Monge 1
  2. Henar Rodríguez-Navarro 1
  3. José-María Marbán 1
  1. 1 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Revista:
Comunicar: Revista Científica de Comunicación y Educación

ISSN: 1134-3478

Año de publicación: 2023

Título del ejemplar: Neurotecnología en el aula: Investigación actual y futuro potencial

Número: 76

Páginas: 47-58

Tipo: Artículo

DOI: 10.3916/C76-2023-04 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Comunicar: Revista Científica de Comunicación y Educación

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 bibliográficas

  • 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
  • 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
  • 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
  • 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), 401-412. https://doi.org/10.1162/jocn_a_01274
  • 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). https://doi.org/10.3390/brainsci11010098
  • Brown, J.S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18, 32-42. https://doi.org/10.3102/0013189X018001032
  • 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
  • Craik, A., He, Y., & Contreras-Vidal, J.J. (2019). Deep learning for electroencephalogram (EEG) classification tasks: A review. Journal of Neural Engineering, 16(3). https://doi.org/10.1088/1741-2552/ab0ab5
  • 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
  • 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
  • Glaser, B., & Strauss, A. (2006). The discovery of grounded theory. Aldine Transaction.
  • 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), 15-15. https://doi.org/10.1038/s41539-021-00094-8
  • Hajare, R., & Kadam, S. (2021). Comparative study analysis of practical EEG sensors in medical diagnoses. Global Transitions Proceedings, 2, 467-475. https://doi.org/10.1016/j.gltp.2021.08.009
  • 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. Psychological Review, 123(5), 620-627. https://doi.org/10.1037/rev0000036
  • Janssen, T.W.P., Grammer, J.K., Bleichner, M.G., Bulgarelli, C., Davidesco, I., Dikker, S., Jasiska, 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
  • 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
  • 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
  • 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. https://doi.org/10.3389/fnins.2017.00109
  • Lau-Zhu, A., Lau, M.P.H., & Mcloughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental Cognitive Neuroscience, 36, 100635-100635. https://doi.org/10.1016/j.dcn.2019.100635
  • Liu, Y., & Zhang, Y. (2021). Developing sustaining authentic partnership between MBE researchers and local schools. Mind, Brain, and Education, 15, 153-162. https://doi.org/10.1111/mbe.12280
  • 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
  • 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
  • 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
  • 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), 5116-5119. https://doi.org/10.1016/0301-0511(95)05116-3
  • 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
  • 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
  • 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
  • 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/10.1080/095183904200025363
  • Stake, R.E. (2010). Qualitative research: Studying how things work. Guilford Publications. https://bit.ly/3J0mmNf
  • 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), 103-103. https://doi.org/10.3390/brainsci12010103
  • 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
  • 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. https://doi.org/10.7717/peerj.9713
  • 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
  • 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
  • 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/https://doi.org.ponton.uva.es/10.1111/mbe.12314
  • 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