Diseño de un sistema de retroalimentación neuronal para el entrenamiento de la meditación basado en electroencefalograma

Resumen

La meditación es una forma de entrenamiento mental que tiene potencial terapéutico y beneficios cognitivos que pueden mejorar la atención, el bienestar mental y la neuroplasticidad en el cerebro. Sin embargo, el proceso de aprendizaje no es fácil porque los meditadores no reciben una retroalimentación inmediata que les permita saber si están realizando correctamente la actividad. El entrenamiento basado en retroalimentación neuronal es una de las técnicas para entrenar la autorregulación del cerebro y tiene el potencial de aumentar la efectividad de la meditación. Sin embargo, los beneficios difieren mucho entre sujetos con un alto porcentaje de ineficacia. En este trabajo, se propone un Sistema de Entrenamiento de Retroalimentación Neuronal basado en una metodología de diseño centrada en el usuario para proporcionar retroalimentación de desempeño en tiempo real a los meditadores para aumentar los niveles de atención y relajación a través de una interfaz de estímulos visuales, sonoros y olfativos. Los niveles de atención y relajación de nueve participantes se midieron con una diadema Neurosky EEG durante la práctica de meditación para analizar la incidencia de cada tipo de estímulo durante la actividad. La retroalimentación de estímulos visuales pudo aumentar los niveles de atención del 78% de los participantes en un 11,8% en comparación con una sesión de meditación sin ningún estímulo. La retroalimentación de los estímulos sonoros logró aumentar los niveles de relajación del 44,4% de los participantes en un 16% en comparación con una sesión sin ningún estímulo. Estos resultados podrían aportar nuevos conocimientos para el diseño de una interfaz de sistema de retroalimentación neuronal el entrenamiento de la meditación. Se sugiere realizar más investigaciones sobre las interfaces de entrenamiento de retroalimentación neuronal para meditadores con el fin de validar estos resultados con más participantes.

Palabras clave

Atención, Electroencefalograma, Meditación, Retroalimentación Neuronal, Relajación, Entrenamiento

Biografía del autor/a

Andrés Eduardo Nieto-Vallejo, M.Sc.

Roles:  Recolección de datos, Análisis formal, Metodología, Software, Validación, Escritura-borrador original, Escritura- revisión edición.

Omar Fernando Ramírez-Pérez, M.Sc.

Roles: Análisis formal, Escritura-borrador original, Escritura- revisión edición.

Luis Eduardo Ballesteros-Arroyave

Roles: Conceptualización, Metodología, Recolección de datos, Escritura-borrador original.

Angela Aragón

Roles: Conceptualización, Metodología, Recolección de datos Escritura-borrador original.

Citas

[1] A. Lutz, H. A. Slagter, J. D. Dunne, R. J. Davidson, “Attention regulation and monitoring in meditation,” Trends in Cognitive Sciences, vol. 12, no. 4, pp. 163-169, 2008. https://doi.org/10.1016/j.tics.2008.01.005

[2] A. P. Jha, J. Krompinger, M. J. Baime, “Mindfulness training modifies subsystems of attention,” Cognitive, Affective, & Behavioral Neuroscience, vol. 7, pp. 109-119, 2007. https://doi.org/10.3758/CABN.7.2.109

[3] A. Fingelkurts, A. Fingelkurts, T. Kallio-Tamminen, “EEG-guided meditation: A personalized approach,” Journal of Physiology-Paris, vol. 109, no. 4–6, pp. 180-190, 2015. https://doi.org/10.1016/j.jphysparis.2015.03.001

[4] T. D. Wager, J. K. Rilling, E. E. Smith, A. Sokolik, K. L. Casey, R. J. Davidson, J. D. Cohen, “Placebo-induced changes in FMRI in the anticipation and experience of pain,” Science, vol. 303, no. 5661, pp. 1162-1167, 2004. https://doi.org/10.1126/science.1093065

[5] R. J. Davidson, J. Kabat-Zinn, J. Schumacher, M. Rosenkranz, D. Muller, S. F. Santorelli, J. F. Sheridan, “Alterations in brain and immune function produced by mindfulness meditation,” Psychosomatic medicine, vol. 65, no. 4, pp. 564-570, 2003. https://doi.org/10.1097/01.psy.0000077505.67574.e3

[6] G. N. Levine, R. A. Lange, C. N. Bairey‐Merz, R. J. Davidson, K. Jamerson, P. K. Mehta, T. Shah, “Meditation and cardiovascular risk reduction: a scientific statement from the American Heart Association,” Journal of the American Heart Association, vol. 6, no. 10, e002218, 2017. https://doi.org/10.1161/JAHA.117.002218

[7] R. J. Davidson, A. Lutz, “Buddha's brain: Neuroplasticity and meditation,” IEEE Signal Processing Magazine, vol. 25, no. 1, pp. 176-174, 2008. https://doi.org/10.1109/msp.2008.4431873

[8] B. Ülker, M. B. Tabakcıoğlu, H. Çizmeci, D. Ayberkin, “Relations of attention and meditation level with learning in engineering education,” in 9th International Conference on Electronics, Computers and Artificial Intelligence, 2017, pp. 1-4. https://doi.org/10.1109/ECAI.2017.8166407

[9] R. van Lutterveld, S. D. Houlihan, P. Pal, M. D. Sacchet, C. McFarlane-Blake, P. R. Patel, J. S. Sullivan, A. Ossadtchi, S. Druker, C. Bauer, J. A. Brewer, “Source-space EEG neurofeedback links subjective experience with brain activity during effortless awareness meditation,” NeuroImage, vol. 151, pp. 117-127, 2017. https://doi.org/10.1016/j.neuroimage.2016.02.047

[10] H. Hunkin, D. L. King, I. Zajac, “EEG Neurofeedback During Focused Attention Meditation: Effects on State Mindfulness and Meditation Experiences,” Mindfulness, vol 12, pp. 841-851, 2020. https://doi.org/10.1007/s12671-020-01541-0

[11] M. Navarro Gil, C. Escolano Marco, J. Montero-Marín, J. Minguez Zafra, E. Shonin, J. García Campayo, “Efficacy of Neurofeedback on the Increase of Mindfulness-Related Capacities in Healthy Individuals: a Controlled Trial”, Mindfulness, vol. 9, pp. 303–311, 2018. https://doi.org/10.1007/s12671-017-0775-1

[12] P. Supoo, P. Sittiprapaporn, "Brainwave Activity and Cognitive Performance Investigated by Meditation Yoga," in 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2019, pp. 482-485. https://doi.org/10.1109/ECTI-CON47248.2019.8955411

[13] P. Stapleton, J. Dispenza, S. McGill, D. Sabot, M. Peach, D. Raynor, “Large effects of brief meditation intervention on EEG spectra in meditation novices,” IBRO Reports, vol. 9, pp. 290-301, 2020. https://doi.org/10.1016/j.ibror.2020.10.006

[14] J. H. Gruzelier, “EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants,” Neuroscience & Biobehavioral Reviews, vol. 44, pp. 124-141, 2014. https://doi.org/10.1016/j.neubiorev.2013.09.015

[15] M. Alimardani, L. Kemmeren, K. Okumura, K. Hiraki, "Robot-Assisted Mindfulness Practice: Analysis of Neurophysiological Responses and Affective State Change," in 29th IEEE International Conference on Robot and Human Interactive Communication, 2020, pp. 683-689. https://doi.org/10.1109/RO-MAN47096.2020.9223428

[16] D. Crivelli, G. Fronda, I. Venturella, M. Balconi, “Supporting Mindfulness Practices with Brain-Sensing Devices. Cognitive and Electrophysiological Evidences,” Mindfulness, vol. 10, pp. 301–311, 2019. https://doi.org/10.1007/s12671-018-0975-3

[17] T. Yamsa-ard, Y. Wongsawat, "The relationship between EEG and binaural beat stimulation in meditation," in 7th Biomedical Engineering International Conference, 2014, pp. 1-4. https://doi.org/10.1109/BMEiCON.2014.7017405

[18] A. Choo, A. May, "Virtual mindfulness meditation: Virtual reality and electroencephalography for health gamification," in IEEE Games Media Entertainment, 2014, pp. 1-3. https://doi.org/10.1109/GEM.2014.7048076

[19] C. Chen, Y. Tang, N. Zhang, J. Shin, "Neurofeedback based attention training for children with ADHD," in IEEE 8th International Conference on Awareness Science and Technology, 2017, pp. 93-97. https://doi.org/10.1109/ICAwST.2017.8256530

[20] T. Wei, C. Young, "A mobile approach for neurofeedback cognitive enhancement," in IEEE International Symposium on Medical Measurements and Applications Proceedings, 2015, pp. 191-195. https://doi.org/10.1109/MeMeA.2015.7145197

[21] Y. Liu, O. Sourina, X. Hou, "Neurofeedback Games to Improve Cognitive Abilities," in International Conference on Cyberworlds, Santander, 2014, pp. 161-168. https://doi.org/10.1109/CW.2014.30

[22] W. L. Lim, O. Sourina, L. Wang, "MIND - An EEG Neurofeedback Multitasking Game," in International Conference on Cyberworlds, 2015, pp. 169-172. https://doi.org/10.1109/CW.2015.39

[23] E. E. Schaefer, “Using Neurofeedback and Mindfulness Pedagogies to Teach Open Listening,” Computers and Composition, vol. 50, pp. 78-104, 2018. https://doi.org/10.1016/j.compcom.2018.07.002

[24] W. Jinn-Rong, H. Shulan, “Neurofeedback training improves attention and working memory performance,” Clinical Neurophysiology, vol. 124, no. 12, pp. 2406-2420, 2013. https://doi.org/10.1016/j.clinph.2013.05.020

[25] G. Huang, J. Liu, L. Li, L. Zhang, Y. Zeng, L. Ren, S. Ye, Z. Zhang, “A novel training-free externally-regulated neurofeedback (ER-NF) system using phase-guided visual stimulation for alpha modulation,” NeuroImage, vol. 189, pp. 688-699, 2019. https://doi.org/10.1016/j.neuroimage.2019.01.072

[26] C. Jeunet, B. Glize, A. McGonigal, J.-M. Batail, J.-A. Micoulaud-Franchi, “Using EEG-based brain computer interface and neurofeedback targeting sensorimotor rhythms to improve motor skills: Theoretical background, applications and prospects,” Neurophysiologie Clinique, vol. 49, no. 2, pp. 125-136, 2019. https://doi.org/10.1016/j.neucli.2018.10.068

[27] G. A. Mendes, L. Cunha de Miranda, “The influence of graphical elements on user’s attention and control on a neurofeedback-based game,” Entertainment Computing, vol. 29, pp. 10-19, 2019. https://doi.org/10.1016/j.entcom.2018.10.003

[28] N. M. C. da Costa, E. G. Bicho, N. S. Dias, "Priming with mindfulness affects our capacity to self-regulate brain activity?," in IEEE 8th International Conference on Serious Games and Applications for Health, 2020, pp. 1-8, https://doi.org/10.1109/SeGAH49190.2020.9201841

[29] G. Polich, S. Gray, D. Tran, L. Morales-Quezada, M. Glenn “Comparing focused attention meditation to meditation with mobile neurofeedback for persistent symptoms after mild-moderate traumatic brain injury: a pilot study,” Brain Injury, vol. 34, pp. 1408-1415, 2020. https://doi.org/10.1080/02699052.2020.1802781

[30] O. Alkoby, A. Abu-Rmileh, O. Shriki, D. Todder, “Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning,” Neuroscience, vol. 378, pp. 155-164, 2018. https://doi.org/10.1016/j.neuroscience.2016.12.050

[31] S. Cook, R. M. Baecker, C. Munteanu, A. Walker, “Towards Technologically Assisted Mindfulness Meditation Practice in Older Adults: An Analysis of Difficulties Faced and Design Suggestions for Neurofeedback," Lecture Notes in Computer Science, vol 10285, pp. 423-442, 2017. https://doi.org/10.1007/978-3-319-58625-0_31

[32] T. Brandmeyer, A. Delorme, “Closed-Loop Frontal Midlineθ Neurofeedback: A Novel Approach for Training Focused-Attention Meditation.” Frontiers in Human Neuroscience, vol 14, pp. 246, 2020. https://www.frontiersin.org/article/10.3389/fnhum.2020.00246

[33] P. Desmet, M. Vastenburg, N. Romero, “Pick-A-Mood; development and application of a pictorial mood-reporting instrument,” in 8th International Conference on Design and Emotion: Out of Control - Proceedings, 2012.