Colombia
| Trabajo de grado - Maestría
Aplicación de la inteligencia computacional en el análisis de datos de electroencefalografía para la clasificación y reconocimiento de estados mentales relacionados con mente divagante y atención plena
dc.contributor | Castillo Ossa, Luis Fernando | |
dc.contributor | Gustavo Isaza | |
dc.contributor | Carlos Ferrin | |
dc.contributor | Jeferson Arango | |
dc.contributor | GITIR Grupo de Investigación en Tecnologías de la Información y Redes (Categoría A) | |
dc.creator | Montes Marín, Leonardo | |
dc.date | 2022-05-02T22:49:40Z | |
dc.date | 2022-05-02T22:49:40Z | |
dc.date | 2022-04-28 | |
dc.date.accessioned | 2023-09-06T18:31:11Z | |
dc.date.available | 2023-09-06T18:31:11Z | |
dc.identifier | https://repositorio.ucaldas.edu.co/handle/ucaldas/17585 | |
dc.identifier | Universidad de Caldas | |
dc.identifier | Repositorio Institucional Universidad de Caldas | |
dc.identifier | https://repositorio.ucaldas.edu.co/ | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8698618 | |
dc.description | Ilustraciones | |
dc.description | spa:La electroencefalografía EEG, se ha convertido en una herramienta de suma importancia en el análisis de la actividad cognitiva, las técnicas basadas en inteligencia artificial han llegado a integrarse como una parte fundamental en los procesos de extracción de patrones, clasificación y aprendizaje de datos de señales EEG. Uno de los fenómenos, objeto de estudio del análisis cerebral a nivel electrofisiológico y clínico es la meditación. Dados sus beneficios comprobados científicamente y su uso potencial para la curación de las condiciones médicas, relacionadas con trastornos cognitivos y el bienestar mental en general. Recientemente se han identificado un cúmulo de investigaciones que involucran recursos de la inteligencia computacional en el análisis de factores asociados al estudio de la mente divagante y la meditación. La meditación como herramienta reguladora de los pensamientos divagantes y su incidencia en la salud del cerebro, está fundamentada en el hecho de que algunos desordenes cognitivos vienen dados por los pensamientos divagantes. El estado de mente divagante se ve expresado mediante la actividad del Default-Mode Network DMN. La meditación regula el estado de DMN. Basados en estos principios se pretende diseñar un modelo e implementar un prototipo para la detección de la mente divagante MW, usando procesamiento de señales EEG y Aprendizaje Automático ML a partir de señales eléctricas producidas por el cerebro. Con el propósito de crear un framework basado en inteligencia artificial, para el análisis datos EEG y caracterización de mente divagante MW, como base de un programa de entrenamiento asistido por computadora para el aprendizaje de la meditación. | |
dc.description | eng:Electroencephalography EEG has become in one main resources in cerebral activity analysis. Artificial Intelligence based techniques has been a fundamental part in pattern detection, classification and learning of EEG signals data. One of the subjects, which is object of study at electrophysiology and clinic level in neural analysis is meditation. Despite of its well-known and scientifically proven benefits and its potential use in medical conditions related with cognitive disorders and mental healthcare. At this point there are a few initiatives involved in the use of Computational Intelligence in the analysis of meditation. The meditation as mind-wandering controller and its relationship with mental wellness, is established in this project by the fact that some mental conditions are increased by mind-wandering MW; this experience arises from activity in the Default Mode Network DMN; Mindfulness meditation regulates the DMN activity. Based on these principles we propose to design a model and implement a prototype for mind-wandering MW classification using EEG signal processing and machine learning ML processing from electrical signals produced by the brain, with the purpose of create an Artificial Intelligence based framework to analyze EEG data and classify MW activity arousal, as a part of a computer-aided meditation training program. | |
dc.description | Título de la tesis / Resumen / Abstract / Índice / Lista de Figuras / Lista de Tablas / Lista de Abreviaturas / 1. CAPITULO 1. Introducción / 1.1. Campo temático / 1.2. Planteamiento del problema / 1.2.1. Descripción de la realidad problemática / 1.2.2. Formulación del problema / 1.2.3. Delimitación de la investigación / 1.3. Justificación / 1.4. Objetivos: / 1.4.1. Objetivo general / 1.4.2. Objetivos específicos / 1.5. Estructura del documento / 2. CAPITULO 2. Revisión bibliográfica / 2.1. Marco teórico / 2.1.1. Procesamiento de Señales Bioeléctricas y Electroencefalografía: / 2.1.2. Interfaces Cerebro Computador BCI / 2.1.3. Procesamiento de señales EEG / 2.1.4. Inteligencia Artificial y Aprendizaje Automático / 2.2. Trabajos Relacionados / 2.2.1. Mindwanering: La mente divagante y el Default Mode Network / 2.2.2. Meditación Mindfulness: Una terapia de relajación de la mente basado en conciencia plena asociada y un fenómeno neurológico en la electroencefalografía / 2.2.3. Inteligencia Artificial y Aprendizaje automático en Interfaces Cerebro Computador / 2.2.4. Interfaces Cerebro Computador en la actualidad / 2.2.5. Teoría y métodos de estimación espectral: Aplicación al análisis EEG / 2.2.6. Referente neurocientífico de la mente divagante, la meditación y el DMN / 3. CAPITULO 3. Metodología: Descripción detallada del proceso / 3.1. Materiales y Métodos / 3.1.1. Establecer los datos provenientes de análisis neuronales con el fin de proyectar experimentos para la detección del Default Mode Network (DMN) / 3.1.2. Evaluar las técnicas de procesamiento de neuroseñales EGG para la detección de DMN a través de dispositivos BCI / 3.1.3. Determinar y aplicar técnicas de análisis de datos a partir de técnicas de clasificación y aprendizaje automático que aproximen la caracterización de mente divagante / 3.1.4. Validar el prototipo del sistema mediante pruebas que se llevaran a cabo sobre la interfaz 112 3.2. Diseño de la solución / 3.2.1. Adquisición de datos / 3.2.2. Preprocesamiento y extracción de características / 3.2.3. Clasificación: Desarrollar un modelo predictivo / 3.2.4. Validación, optimizar del modelo y despliegue / 4. CAPITULO 4. Análisis de Resultados / 4.1. Estudio Entre-sujetos: / 4.2. Estudio Intra-sujetos: / 5. CAPÍTULO 5. Trabajo futuro, conclusiones y recomendaciones / 5.1. Diseñar un modelo e implementar un prototipo computacional, para la detección de estado de mente divagante, que contribuya en el aprendizaje de la meditación / 5.2. Establecer los datos provenientes de análisis neuronales con el fin de proyectar experimentos para la detección del Default Mode Network (DMN) / 5.3. Evaluar las técnicas de procesamiento de neuroseñales EGG para la detección de DMN a través de dispositivos BCI / 5.4. Determinar y aplicar técnicas de análisis de datos a partir de técnicas de clasificación y aprendizaje automático que aproximen la caracterización de mente divagante / 5.5. Validar el prototipo del sistema mediante pruebas que se llevaran a cabo sobre la interfaz / . 130 5.6. Trabajo Futuro / Anexos / Anexo 1 / Anexo 2 / Anexo 3 /Anexo 4 / Anexo 5 / Bibliografía / Referencias | |
dc.description | Maestría | |
dc.description | Magister en Ingeniería Computacional | |
dc.description | Modelos Biocomputacionales y Bioinformática | |
dc.description | Inteligencia Computacional y Organizacional | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.language | eng | |
dc.language | spa | |
dc.publisher | Facultad de Ingeniería | |
dc.publisher | Manizales | |
dc.publisher | Maestría en Ingeniería Computacional | |
dc.relation | a, C., Brown, K. W., & Ryan, R. M. (2011). The benefits of being present: Mindfulness and its role in psychological well-being. Rehabilitation, 84(c), 2011–2011. https://doi.org/10.1023/A | |
dc.relation | Abercrombie, H. C., Schaefer, S. M., Larson, C. L., Oakes, T. R., Lindgren, K. A., Holden, J. E., Perlman, S. B., Turski, P. A., Krahn, D. D., Benca, R. M., & Davidson, R. J. (1998). Metabolic rate in the right amygdala predicts negative affect in depressed patients. Neuroreport, 9(14), 3301–3307. | |
dc.relation | Acı, Ç. İ., Kaya, M., & Mishchenko, Y. (2019). Distinguishing mental attention states of humans via an EEG-based passive BCI using machine learning methods. Expert Systems with Applications, 134, 153–166. https://doi.org/10.1016/j.eswa.2019.05.057 | |
dc.relation | Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6). https://doi.org/10.1109/TKDE.2005.99 | |
dc.relation | Aliño, M., Gadea Doménech, M., Pérez San Miguel, J., & Espert Tortajada, R. (2015). Ayahuasca: farmacología, efectos agudos, potencial terapéutico y rituales. In Revista española de drogodependencias (Issue 1, pp. 75–91). Asociación Española de Estudio en Drogodependencias, AESED. | |
dc.relation | American Academy of Neurology., A., Griffis, J., Visscher, K., Dobbins, A., Gawne, T., DiFrancesco, M., & Szaflarski, J. (2015). Alpha Rhythm and the Default Mode Network: An EEG-fMRI Study. Neurology, 84(14 Supplement), P6.021. http://www.neurology.org/content/84/14_Supplement/P6.021 | |
dc.relation | Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V. P., Kalnin, A., & Lowe, M. J. (2005a). Activity and connectivity of brain mood regulating circuit in depression: A functional magnetic resonance study. Biological Psychiatry, 57(10), 1079–1088. https://doi.org/10.1016/j.biopsych.2005.02.021 | |
dc.relation | Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V. P., Kalnin, A., & Lowe, M. J. (2005b). Activity and connectivity of brain mood regulating circuit in depression: a functional magnetic resonance study. Biological Psychiatry, 57(10), 1079–1088. https://doi.org/10.1016/j.biopsych.2005.02.021 | |
dc.relation | Anderson, N. D., Lau, M. A., Segal, Z. v., & Bishop, S. R. (2007). Mindfulness-based stress reduction and attentional control. Clinical Psychology & Psychotherapy, 14(6), 449–463. https://doi.org/10.1002/cpp.544 | |
dc.relation | Aura Health. (2017). Aura: Best Mindfulness Meditation App for Stress and Anxiety. https://www.aurahealth.io/ | |
dc.relation | Austin, J. H. (2006). Zen-Brain Reflections. http://www.elibrary.ibc.ac.th/files/private/Zen-Brain Reflections.pdf | |
dc.relation | Baer, R. a. (2003). Mindfulness Training as a Clinical Intervention : In Clinical Psychology: Science and Practice (Issue 1998, pp. 125–143). https://doi.org/10.1093/clipsy/bpg015 | |
dc.relation | Bao, F. S., Liu, X., & Zhang, C. (2011). PyEEG: an open source Python module for EEG/MEG feature extraction. Computational Intelligence and Neuroscience, 2011, 406391. https://doi.org/10.1155/2011/406391 | |
dc.relation | Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2012). Multiclass Brain–Computer Interface Classification by Riemannian Geometry. IEEE Transactions on Biomedical Engineering, 59(4), 920–928. https://doi.org/10.1109/TBME.2011.2172210 | |
dc.relation | Barachant, A., Bonnet, S., Congedo, M., & Jutten, C. (2013). Classification of covariance matrices using a Riemannian-based kernel for BCI applications. Neurocomputing, 112, 172–178. https://doi.org/10.1016/j.neucom.2012.12.039 | |
dc.relation | Barascud Nicolas. (2021). MEEGkit: EEG and MEG denoising in Python. https://nbara.github.io/python-meegkit/modules/meegkit.asr.html | |
dc.relation | Barlow John. (1993). The Electroencephalogram. Its Patterns and Origins. The MIT Press. https://mitpress.mit.edu/books/electroencephalogram | |
dc.relation | Beninger, J., Hamilton-Wright, A., Walker, H. E. K., & Trick, L. M. (2021). Machine learning techniques to identify mind-wandering and predict hazard response time in fully immersive driving simulation. Soft Computing, 25(2). https://doi.org/10.1007/s00500-020-05217-8 | |
dc.relation | Berkovich-Ohana, A., Glicksohn, J., & Goldstein, A. (2014). Studying the default mode and its mindfulness-induced changes using EEG functional connectivity. Social Cognitive and Affective Neuroscience, 9(10), 1616–1624. https://doi.org/10.1093/scan/nst153 | |
dc.relation | Bhuvaneswari, P., & Kumar, J. S. (2015). Influence of Linear Features in Nonlinear Electroencephalography (EEG) Signals. Procedia Computer Science, 47, 229–236. https://doi.org/10.1016/j.procs.2015.03.202 | |
dc.relation | Bishop, S. R., Lau, M., Shapiro, S., Carlson, L., Anderson, N. D., Carmody, J., Segal, Z. v., Abbey, S., Speca, M., Velting, D., & Devins, G. (2004). Mindfulness: A proposed operational definition. Clinical Psychology: Science and Practice, 11(3), 230–241. https://doi.org/10.1093/clipsy/bph077 | |
dc.relation | Blum, S., Jacobsen, N. S. J., Bleichner, M. G., & Debener, S. (2019). A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact Handling. Frontiers in Human Neuroscience, 13. https://doi.org/10.3389/fnhum.2019.00141 | |
dc.relation | Brandmeyer, T., & Delorme, A. (2018). Reduced mind wandering in experienced meditators and associated EEG correlates. Experimental Brain Research, 236(9). https://doi.org/10.1007/s00221-016-4811-5 | |
dc.relation | Brefczynski-Lewis, J. a, Lutz, a, Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. https://doi.org/10.1073/pnas.0606552104 | |
dc.relation | Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007a). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. https://doi.org/10.1073/pnas.0606552104 | |
dc.relation | Brefczynski-Lewis, J. A., Lutz, A., Schaefer, H. S., Levinson, D. B., & Davidson, R. J. (2007b). Neural correlates of attentional expertise in long-term meditation practitioners. Proceedings of the National Academy of Sciences of the United States of America, 104(27), 11483–11488. https://doi.org/10.1073/pnas.0606552104 | |
dc.relation | Brown, K. W., & Ryan, R. M. (n.d.). The benefits of being present: Mindfulness and its role in psychological well-being. | |
dc.relation | Buckner, R. L., Jessica, A.-H., Daneil, S., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network Anatomy, function, and consequence. Annals of the New York Academy of Sciences, 1124, 1–38. https://doi.org/10.1196/annals.1440.011 | |
dc.relation | Cabañero, L., Hervás, R., Bravo, J., Rodríguez-Benitez, L., & Nugent, C. (2019). eeglib: computational analysis of cognitive performance during the use of video games. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01592-9 | |
dc.relation | Cao, Z. (2020). A review of artificial intelligence for EEG‐based brain−computer interfaces and applications. Brain Science Advances, 6(3), 162–170. https://doi.org/10.26599/BSA.2020.9050017 | |
dc.relation | Carmody, J., & Baer, R. a. (2008). Relationships between mindfulness practice and levels of mindfulness, medical and psychological symptoms and well-being in a mindfulness-based stress reduction program. Journal of Behavioral Medicine, 31(1), 23–33. https://doi.org/10.1007/s10865-007-9130-7 | |
dc.relation | Center for Investigating Healthy Minds. (2015). Center for Investigating Healthy Minds - Center Founder, Dr. Richard J. Davidson. http://www.investigatinghealthyminds.org/cihmDrDavidson.html | |
dc.relation | Center for Open Science. (2021). OSF Center for Open Science. Https://Osf.Io/. https://osf.io/ | |
dc.relation | Chang, C.-Y., Hsu, S.-H., Pion-Tonachini, L., & Jung, T.-P. (2020). Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings. IEEE Transactions on Biomedical Engineering, 67(4), 1114–1121. https://doi.org/10.1109/TBME.2019.2930186 | |
dc.relation | Chen, A. C. N., Feng, W., Zhao, H., Yin, Y., & Wang, P. (2008). EEG default mode network in the human brain: Spectral regional field powers. NeuroImage, 41(2), 561–574. https://doi.org/10.1016/j.neuroimage.2007.12.064 | |
dc.relation | Chen, X., Chen, N.-X., Shen, Y.-Q., Li, H.-X., Li, L., Lu, B., Zhu, Z.-C., Fan, Z., & Yan, C.-G. (2020). The subsystem mechanism of default mode network underlying rumination: A reproducible neuroimaging study. NeuroImage, 221, 117185. https://doi.org/10.1016/j.neuroimage.2020.117185 | |
dc.relation | Chen, Y.-T., Lee, H.-H., Shih, C.-Y., Chen, Z.-L., Beh, W.-K., Yeh, S.-L., & Wu, A.-Y. (2020a). An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database. | |
dc.relation | Chen, Y.-T., Lee, H.-H., Shih, C.-Y., Chen, Z.-L., Beh, W.-K., Yeh, S.-L., & Wu, A.-Y. (2020b). An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database. | |
dc.relation | Chiesa, A., & Serretti, A. (2009). Mindfulness-Based Stress Reduction for Stress Management in Healthy People: A Review and Meta-Analysis. http://online.liebertpub.com/doi/abs/10.1089/acm.2008.0495 | |
dc.relation | Christoff, K., Gordon, A. M., Smallwood, J., Smith, R., & Schooler, J. W. (2009). Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proceedings of the National Academy of Sciences of the United States of America, 106(21), 8719–8724. https://doi.org/10.1073/pnas.0900234106 | |
dc.relation | Combrisson, E., & Jerbi, K. (2015). Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. Journal of Neuroscience Methods, 250, 126–136. https://doi.org/10.1016/j.jneumeth.2015.01.010 | |
dc.relation | Craven W. Mark, W. S. J. (n.d.). Using Neuronal Networks for Data Mining. 01 | |
dc.relation | Dehuri, S., Jagadev, A. K., & Cho, S.-B. (2013). Epileptic Seizure Identification from Electroencephalography Signal Using DE-RBFNs Ensemble. Procedia Computer Science, 23, 84– 95. https://doi.org/10.1016/j.procs.2013.10.012 | |
dc.relation | Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 | |
dc.relation | Demšar J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research, 7, 1–30. | |
dc.relation | Dong, H. W., Mills, C., Knight, R. T., & Kam, J. W. Y. (2021). Detection of mind wandering using EEG: Within and across individuals. PLOS ONE, 16(5). https://doi.org/10.1371/journal.pone.0251490 | |
dc.relation | Ekman, P., Davidson, R. J., Ricard, M., & Alan Wallace, B. (2005). Buddhist and Psychological Perspectives on Emotions and Well-Being. Current Directions in Psychological Science, 14(2), 59–63. https://doi.org/10.1111/j.0963-7214.2005.00335.x | |
dc.relation | Ekman, P., Davidson, R. J., Ricard, M., Wallace, B. A., & Davidson, J. (2014). Buddhist and Psychological Perspectives Psychological on Emotions and Well-being. Current Directions in Psychological Science, 14(2), 59–63. | |
dc.relation | Elahi, Z., Boostani, R., & Motie Nasrabadi, A. (2013). Estimation of hypnosis susceptibility based on electroencephalogram signal features. Scientia Iranica, 20(3), 730–737. https://doi.org/10.1016/j.scient.2012.07.015 | |
dc.relation | Farb, N. a S., Segal, Z. v., Mayberg, H., Bean, J., Mckeon, D., Fatima, Z., & Anderson, A. K. (2007). Attending to the present: Mindfulness meditation reveals distinct neural modes of selfreference. Social Cognitive and Affective Neuroscience, 2(4), 313–322. https://doi.org/10.1093/scan/nsm030 | |
dc.relation | Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. https://doi.org/10.1016/j.patrec.2005.10.010 | |
dc.relation | Ferrin Bolaños, C., Loaiza-Correa, H., Pierre-Díaz, J., & Vélez-Ángel, P. (2019). Evaluación del aporte de la covarianza de las señales electroencefalográficas a las interfaces cerebro-computador de imaginación motora para pacientes con lesiones de médula espinal. TecnoLógicas, 22(46), 213–231. https://doi.org/10.22430/22565337.1392 | |
dc.relation | Fingelkurts, A. A., Fingelkurts, A. A., & Kallio-Tamminen, T. (2015). EEG-guided meditation: A personalized approach. Journal of Physiology-Paris, 109(4–6), 180–190. https://doi.org/10.1016/j.jphysparis.2015.03.001 | |
dc.relation | Flook, L., Goldberg, S. B., Pinger, L., Bonus, K., & Davidson, R. J. (2013). Mindfulness for teachers: A pilot study to assess effects on stress, burnout and teaching efficacy. Mind, Brain and Education : The Official Journal of the International Mind, Brain, and Education Society, 7(3), 182–195. https://doi.org/10.1111/mbe.12026 | |
dc.relation | Fomina, T., Hohmann, M., Scholkopf, B., & Grosse-Wentrup, M. (2015). Identification of the Default Mode Network with electroencephalography. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 7566–7569. https://doi.org/10.1109/EMBC.2015.7320143 | |
dc.relation | Garrison, K. A., Zeffiro, T. A., Scheinost, D., Constable, R. T., & Brewer, J. A. (2015). Meditation leads to reduced default mode network activity beyond an active task. Cognitive, Affective & Behavioral Neuroscience. https://doi.org/10.3758/s13415-015-0358-3 | |
dc.relation | Gasser, T., & Molinari, L. (1996). The analysis of the EEG. Statistical Methods in Medical Research, 5(1), 67–99. https://doi.org/10.1177/096228029600500105 | |
dc.relation | Gennady G. Knyazev, Jaroslav Y. Slobodskoj-Plusnin, Andrey V. Bocharov, L. V. P. (2011). The default mode network and EEG alpha oscillations: An independent component analysis. Brain Research, 1402, 67–69. http://www.sciencedirect.com/science/article/pii/S0006899311009863 | |
dc.relation | Ghassemi, F., Hassan_Moradi, M., Tehrani-Doost, M., & Abootalebi, V. (2012). Using non-linear features of EEG for ADHD/normal participants’ classification. Procedia - Social and Behavioral Sciences, 32, 148–152. https://doi.org/10.1016/j.sbspro.2012.01.024 | |
dc.relation | Goldin, P. R., & Gross, J. J. (2010). Effects of mindfulness-based stress reduction (MBSR) on emotion regulation in social anxiety disorder. Emotion (Washington, D.C.), 10(1), 83–91. https://doi.org/10.1037/a0018441 | |
dc.relation | Goleman, D., & Davidson, R. (2017). Altered Traits: Science Reveals How Meditation Changes Your Mind, Brain, and Body. | |
dc.relation | Gotink, R. A., Meijboom, R., Vernooij, M. W., Smits, M., & Hunink, M. G. M. (2016). 8-week Mindfulness Based Stress Reduction induces brain changes similar to traditional long-term meditation practice – A systematic review. Brain and Cognition, 108, 32–41. https://doi.org/10.1016/j.bandc.2016.07.001 | |
dc.relation | Goyal, M., Singh, S., Sibinga, E. M. S., Gould, N. F., Rowland-Seymour, A., Sharma, R., Berger, Z., Sleicher, D., Maron, D. D., Shihab, H. M., Ranasinghe, P. D., Linn, S., Saha, S., Bass, E. B., & Haythornthwaite, J. A. (2014). Meditation programs for psychological stress and well-being: a systematic review and meta-analysis. JAMA Internal Medicine, 174(3), 357–368. https://doi.org/10.1001/jamainternmed.2013.13018 | |
dc.relation | Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00267 | |
dc.relation | Grandchamp, R., Braboszcz, C., & Delorme, A. (2014). Oculometric variations during mind wandering. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00031 | |
dc.relation | Granville, V. (2017). Machine Learning Summarized in One Picture - Data Science Central. https://www.datasciencecentral.com/profiles/blogs/machine-learning-summarized-in-onepicture?utm_content=buffer0d5bf&utm_medium=social&utm_source=twitter.com&utm_ca mpaign=buffer | |
dc.relation | Grégoire Cattan, Pedro Rodrigues, & Marco Congedo. (2018). EEG Alpha Waves dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.2348892 | |
dc.relation | Groot, J. M., Boayue, N. M., Csifcsák, G., Boekel, W., Huster, R., Forstmann, B. U., & Mittner, M. (2021). Probing the neural signature of mind wandering with simultaneous fMRI-EEG and pupillometry. NeuroImage, 224. https://doi.org/10.1016/j.neuroimage.2020.117412 | |
dc.relation | Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. v, Hanson, S. J., & Pollmann, S. (2009). PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3, 3. https://doi.org/10.3389/neuro.11.003.2009 | |
dc.relation | Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/s41586-020-2649-2 | |
dc.relation | Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer New York. https://doi.org/10.1007/978-0-387-84858-7 | |
dc.relation | Hjorth, B. (1970). EEG analysis based on time domain properties. Electroencephalography and Clinical Neurophysiology, 29(3), 306–310. https://doi.org/10.1016/0013-4694(70)90143-4 | |
dc.relation | Hosseini, S., & Guo, X. (2019). Deep Convolutional Neural Network for Automated Detection of Mind Wandering using EEG Signals. | |
dc.relation | . https://physionet.org/. (n.d.). https://physionet.org/. Retrieved May 10, 2021, from https://physionet.org/content/?topic=EEG | |
dc.relation | Ichimaru, Y., & Moody, G. B. (1999). Development of the polysomnographic database on CD‐ROM. Psychiatry and Clinical Neurosciences, 53(2). https://doi.org/10.1046/j.1440- 1819.1999.00527.x | |
dc.relation | Irrmischer, M., Houtman, S. J., Mansvelder, H. D., Tremmel, M., Ott, U., & Linkenkaer‐Hansen, K. (2018). Controlling the Temporal Structure of Brain Oscillations by Focused Attention Meditation. Human Brain Mapping, 39(4), 1825–1838. https://doi.org/10.1002/hbm.23971 | |
dc.relation | Jang, J. H., Jung, W. H., Kang, D. H., Byun, M. S., Kwon, S. J., Choi, C. H., & Kwon, J. S. (2011a). Increased default mode network connectivity associated with meditation. Neuroscience Letters, 487(3), 358–362. https://doi.org/10.1016/j.neulet.2010.10.056 | |
dc.relation | Jang, J. H., Jung, W. H., Kang, D.-H., Byun, M. S., Kwon, S. J., Choi, C.-H., & Kwon, J. S. (2011b). Increased default mode network connectivity associated with meditation. Neuroscience Letters, 487(3), 358–362. https://doi.org/10.1016/j.neulet.2010.10.056 | |
dc.relation | JEN L. TEE, & WAI Y. LEONG. (2018). EEG extraction for meditation. Journal of Engineering Science and Technology, 13(7), 2125 – 2135. | |
dc.relation | Jia, H. (2011). Neural network in the application of EEG signal classification method. 2011 Seventh International Conference on Computational Intelligence and Security, 1325–1327. https://doi.org/10.1109/CIS.2011.294 | |
dc.relation | Jin C. (2021). Detecting Mind-Wandering with Machine Learning: Discovering the Neural Correlates of Mind-Wandering Through Generalizable Machine Learning Classifiers with EEG. https://doi.org/10.33612/diss.171835555 | |
dc.relation | Jin, C. Y., Borst, J. P., & van Vugt, M. K. (2019). Predicting task-general mind-wandering with EEG. Cognitive, Affective, & Behavioral Neuroscience, 19(4). https://doi.org/10.3758/s13415-019- 00707-1 | |
dc.relation | Jin, C. Y., Borst, J. P., & Vugt, M. K. (2020). Distinguishing vigilance decrement and low task demands from mind‐wandering: A machine learning analysis of EEG. European Journal of Neuroscience, 52(9). https://doi.org/10.1111/ejn.14863 | |
dc.relation | Kabat-Zinn, J. (2006). Mindfulness-Based Interventions in Context: Past, Present, and Future. Clinical Psychology: Science and Practice, 10(2), 144–156. https://doi.org/10.1093/clipsy.bpg016 | |
dc.relation | kaggle. (2019). Meditation-EEG-Data. https://www.kaggle.com/abyssjumper/meditationeegdata/code | |
dc.relation | Kaggle. (2021). Kaggle: Your Machine Learning and Data Science Community. https://www.kaggle.com/ | |
dc.relation | Kaur, C., & Singh, P. (2015). EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges. Advances in Preventive Medicine, 2015. https://doi.org/10.1155/2015/614723 | |
dc.relation | Kawashima, I., & Kumano, H. (2017). Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00365 | |
dc.relation | Kim, D.-K., Lee, K.-M., Kim, J., Whang, M.-C., & Kang, S. W. (2013). Dynamic correlations between heart and brain rhythm during Autogenic meditation. Frontiers in Human Neuroscience, 7, 414. https://doi.org/10.3389/fnhum.2013.00414 | |
dc.relation | Kim, D.-K., Rhee, J.-H., & Kang, S. W. (2014). Reorganization of the brain and heart rhythm during autogenic meditation. Frontiers in Integrative Neuroscience, 7, 109. https://doi.org/10.3389/fnint.2013.00109 | |
dc.relation | Kim, H. (2010). Dissociating the roles of the default-mode, dorsal, and ventral networks in episodic memory retrieval. NeuroImage, 50(4), 1648–1657. https://doi.org/10.1016/j.neuroimage.2010.01.051 | |
dc.relation | Kim, H. (2012). A dual-subsystem model of the brain’s default network: self-referential processing, memory retrieval processes, and autobiographical memory retrieval. NeuroImage, 61(4), 966– 977. https://doi.org/10.1016/j.neuroimage.2012.03.025 | |
dc.relation | Kirk, U., & Montague, P. R. (2015). Mindfulness meditation modulates reward prediction errors in a passive conditioning task. Frontiers in Psychology, 6, 90. https://doi.org/10.3389/fpsyg.2015.00090 | |
dc.relation | Knyazev, G. G., Slobodskoj-Plusnin, J. Y., Bocharov, A. v., & Pylkova, L. v. (2011). The default mode network and EEG alpha oscillations: An independent component analysis. Brain Research, 1402, 67–79. https://doi.org/10.1016/j.brainres.2011.05.052 | |
dc.relation | Koles, Z. J., Lazar, M. S., & Zhou, S. Z. (1990). Spatial patterns underlying population differences in the background EEG. Brain Topography, 2(4), 275–284. https://doi.org/10.1007/BF01129656 | |
dc.relation | Koszycki, D., Raab, K., Aldosary, F., & Bradwejn, J. (2010). A multifaith spiritually based intervention for generalized anxiety disorder: A pilot randomized trial. Journal of Clinical Psychology, 66(4), 430–441. https://doi.org/10.1002/jclp | |
dc.relation | Kothe, C. (2012). Introduction To Modern Brain-Computer Interface Design - SCCN. Swartz Center for Computational Neuroscience. https://sccn.ucsd.edu/wiki/Introduction_To_Modern_BrainComputer_Interface_Design | |
dc.relation | Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer New York. https://doi.org/10.1007/978-1-4614-6849-3 | |
dc.relation | Lahane, P., & Sangaiah, A. K. (2015). An Approach to EEG Based Emotion Recognition and Classification Using Kernel Density Estimation. Procedia Computer Science, 48, 574–581. https://doi.org/10.1016/j.procs.2015.04.138 | |
dc.relation | Lawhern, V., Hairston, W. D., McDowell, K., Westerfield, M., & Robbins, K. (2012). Detection and classification of subject-generated artifacts in EEG signals using autoregressive models. Journal of Neuroscience Methods, 208(2), 181–189. https://doi.org/10.1016/j.jneumeth.2012.05.017 | |
dc.relation | Lin, H., & Li, Y. (2017). Using EEG Data Analytics to Measure Meditation. https://doi.org/10.1007/978-3-319-58466-9_25 | |
dc.relation | Lomas, T., Ivtzan, I., & Fu, C. H. Y. (2015a). A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience & Biobehavioral Reviews, 57, 401–410. https://doi.org/10.1016/j.neubiorev.2015.09.018 | |
dc.relation | Lomas, T., Ivtzan, I., & Fu, C. H. Y. (2015b). A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience & Biobehavioral Reviews, 57, 401–410. https://doi.org/10.1016/j.neubiorev.2015.09.018 | |
dc.relation | Lomas, T., Ivtzan, I., & Fu, C. H. Y. (2015c). A systematic review of the neurophysiology of mindfulness on EEG oscillations. Neuroscience & Biobehavioral Reviews, 57, 401–410. https://doi.org/10.1016/j.neubiorev.2015.09.018 | |
dc.relation | Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018a). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of Neural Engineering, 15(3), 031005. https://doi.org/10.1088/1741- 2552/aab2f2 | |
dc.relation | Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018b). A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. Journal of Neural Engineering, 15(3), 031005. https://doi.org/10.1088/1741- 2552/aab2f2 | |
dc.relation | Lutz, A., Greischar, L. L., Rawlings, N. B., Ricard, M., & Davidson, R. J. (2004). Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proceedings of the National Academy of Sciences of the United States of America, 101(46), 16369–16373. https://doi.org/10.1073/pnas.0407401101 | |
dc.relation | Lutz, J., Brühl, A. B., Scheerer, H., Jäncke, L., & Herwig, U. (2016a). Neural correlates of mindful selfawareness in mindfulness meditators and meditation-naïve subjects revisited. Biological Psychology, 119, 21–30. https://doi.org/10.1016/j.biopsycho.2016.06.010 | |
dc.relation | Lutz, J., Brühl, A. B., Scheerer, H., Jäncke, L., & Herwig, U. (2016b). Neural correlates of mindful selfawareness in mindfulness meditators and meditation-naïve subjects revisited. Biological Psychology, 119, 21–30. https://doi.org/10.1016/j.biopsycho.2016.06.010 | |
dc.relation | Maini, V. (2017). A Beginner’s Guide to AI/ML – Machine Learning for Humans. https://medium.com/machine-learning-for-humans/why-machine-learning-matters6164faf1df12?source=collection_home---4------0--------------------- | |
dc.relation | Maniar, S., Kalra, R., Shinaman, R., & Longton William. (2017). WellBrain. http://wellbrain.io/ | |
dc.relation | Manuello, J., Vercelli, U., Nani, A., Costa, T., & Cauda, F. (2016). Mindfulness meditation and consciousness: An integrative neuroscientific perspective. Consciousness and Cognition, 40, 67–78. https://doi.org/10.1016/j.concog.2015.12.005 | |
dc.relation | Mathworks. (2017). Machine Learning with MATLAB. https://la.mathworks.com/campaigns/offers/machine-learning-withmatlab.html?s_tid=hp_offer_ml_ebok | |
dc.relation | Mayberg, H. S., Lozano, A. M., Voon, V., McNeely, H. E., Seminowicz, D., Hamani, C., Schwalb, J. M., & Kennedy, S. H. (2005). Deep brain stimulation for treatment-resistant depression. Neuron, 45(5), 651–660. https://doi.org/10.1016/j.neuron.2005.02.014 | |
dc.relation | McBride, J. C., Zhao, X., Munro, N. B., Jicha, G. A., Schmitt, F. A., Kryscio, R. J., Smith, C. D., & Jiang, Y. (2015). Sugihara causality analysis of scalp EEG for detection of early Alzheimer’s disease. NeuroImage: Clinical, 7, 258–265. https://doi.org/10.1016/j.nicl.2014.12.005 | |
dc.relation | McKenna, D. J. (2004). Clinical investigations of the therapeutic potential of ayahuasca: rationale and regulatory challenges. Pharmacology & Therapeutics, 102(2), 111–129. https://doi.org/10.1016/j.pharmthera.2004.03.002 | |
dc.relation | McKinney, W. (2010). Data Structures for Statistical Computing in Python. 56–61. https://doi.org/10.25080/Majora-92bf1922-00a | |
dc.relation | Mitchell, T. M. (1997). Machine Learning (McGraw-Hill International Editions McGraw-Hill international editions - computer science series McGraw-Hill series in artificial intelligence McGraw-Hill series in computer science McGraw-Hill series in computer science: Artificial intelligence, Ed.). | |
dc.relation | Mohammadi, M., Al-Azab, F., Raahemi, B., Richards, G., Jaworska, N., Smith, D., de la Salle, S., Blier, P., & Knott, V. (2015). Data mining EEG signals in depression for their diagnostic value. BMC Medical Informatics and Decision Making, 15, 108. https://doi.org/10.1186/s12911-015-0227- 6 | |
dc.relation | Moody, G. B., Mark, R. G., & Goldberger, A. L. (2001). PhysioNet: a Web-based resource for the study of physiologic signals. IEEE Engineering in Medicine and Biology Magazine, 20(3). https://doi.org/10.1109/51.932728 | |
dc.relation | Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., Jung, T.-P., & Cauwenberghs, G. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Transactions on Biomedical Engineering, 62(11), 2553–2567. https://doi.org/10.1109/TBME.2015.2481482 | |
dc.relation | Neuner, I., Arrubla, J., Werner, C. J., Hitz, K., Boers, F., Kawohl, W., & Shah, N. J. (2014). The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study. PloS One, 9(2), e88214. https://doi.org/10.1371/journal.pone.0088214 | |
dc.relation | Neuralink. (n.d.). Home - Neuralink. Retrieved May 23, 2021, from https://neuralink.com/ | |
dc.relation | Oken, B., Ahani, A., Wahbeh, H., Nezamfar, H., Miller, M., Erdogmus, D., & Goodrich, E. (2014). Signal Processing and Machine Learning of EEG and Respiration Changes During Mindfulness Meditation State. The Journal of Alternative and Complementary Medicine, 20(5), A25–A25. https://doi.org/10.1089/acm.2014.5061.abstract | |
dc.relation | Orhan, U., Hekim, M., & Ozer, M. (2011). EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems With Applications, 38, 13475– 13481. https://doi.org/10.1016/j.eswa.2011.04.149 | |
dc.relation | Othman, M., Wahab, A., Karim, I., Dzulkifli, M. A., & Alshaikli, I. F. T. (2013). EEG Emotion Recognition Based on the Dimensional Models of Emotions. Procedia - Social and Behavioral Sciences, 97, 30–37. https://doi.org/10.1016/j.sbspro.2013.10.201 | |
dc.relation | Parvinnia, E., Sabeti, M., Zolghadri Jahromi, M., & Boostani, R. (2014). Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm. Journal of King Saud University - Computer and Information Sciences, 26(1), 1–6. https://doi.org/10.1016/j.jksuci.2013.01.001 | |
dc.relation | Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., & Prettenhofer, P. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830. | |
dc.relation | PhysioNet. (n.d.). AHA Database Sample Excluded Record. Retrieved May 17, 2021, from https://physionet.org/content/ahadb/1.0.0/ | |
dc.relation | Pizzagalli, D. A. (2011). Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology : Official Publication of the American College of Neuropsychopharmacology, 36(1), 183–206. https://doi.org/10.1038/npp.2010.166 | |
dc.relation | Prerau, M. J., Brown, R. E., Bianchi, M. T., Ellenbogen, J. M., & Purdon, P. L. (2017). Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis. Physiology, 32(1), 60–92. https://doi.org/10.1152/physiol.00062.2015 | |
dc.relation | Raichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience. https://doi.org/10.1146/annurev-neuro-071013-014030 | |
dc.relation | Rechy-Ramirez, E. J., & Hu, H. (2015). Bio-signal based control in assistive robots: a survey. Digital Communications and Networks, 1(2), 85–101. https://doi.org/10.1016/j.dcan.2015.02.004 | |
dc.relation | Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009). xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface. IEEE Transactions on Biomedical Engineering, 56(8), 2035–2043. https://doi.org/10.1109/TBME.2009.2012869 | |
dc.relation | Rodriguez‐Larios, J., & Alaerts, K. (2021). EEG alpha–theta dynamics during mind wandering in the context of breath focus meditation: An experience sampling approach with novice meditation practitioners. European Journal of Neuroscience, 53(6). https://doi.org/10.1111/ejn.15073 | |
dc.relation | Saha, S., Mamun, K. A., Ahmed, K., Mostafa, R., Naik, G. R., Darvishi, S., Khandoker, A. H., & Baumert, M. (2021). Progress in Brain Computer Interface: Challenges and Opportunities. Frontiers in Systems Neuroscience, 15. https://doi.org/10.3389/fnsys.2021.578875 | |
dc.relation | Scheeringa, R., Bastiaansen, M. C. M., Petersson, K. M., Oostenveld, R., Norris, D. G., & Hagoort, P. (2008). Frontal theta EEG activity correlates negatively with the default mode network in resting state. International Journal of Psychophysiology, 67(3), 242–251. https://doi.org/10.1016/j.ijpsycho.2007.05.017 | |
dc.relation | Shams, W. K., Wahab, A., & Fakhri, I. (2013). Affective Computing Model Using Source-temporal Domain. Procedia - Social and Behavioral Sciences, 97, 54–62. https://doi.org/10.1016/j.sbspro.2013.10.204 | |
dc.relation | Shneiderman, B., & Plaisant, C. (2009). Designing the User Interface: Strategies for Effective HumanComputer Interaction. Nature Publishing Group. | |
dc.relation | Shoemaker, A. (2014). Ayahuasca Medicine: The Shamanic World of Amazonian Sacred Plant Healing. https://doi.org/9781620551936 | |
dc.relation | Silicon Valley Bank. (2018). China Startup Outlook 2018 Key insights from the Silicon Valley Bank Startup Outlook Survey. https://www.svb.com/uploadedFiles/Content/Trends_and_Insights/Reports/Startup_Outloo k_Report/China/SVB-SUO-Chinareport.pdf?utm_content=buffer1cf63&utm_medium=social&utm_source=twitter.com&utm_ campaign=buffer | |
dc.relation | Simon, R. (2015). The default mode network as a biomarker for monitoring the therapeutic effects of meditation. 6(June), 1–10. https://doi.org/10.3389/fpsyg.2015.00776 | |
dc.relation | Smallwood, J., Fitzgerald, A., Miles, L. K., & Phillips, L. H. (2009). Shifting moods, wandering minds: negative moods lead the mind to wander. Emotion (Washington, D.C.), 9(2), 271–276. https://doi.org/10.1037/a0014855 | |
dc.relation | Sörnmo, L., & Laguna, P. (2005). Bioelectrical Signal Processing in Cardiac and Neurological Applications. In Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier Inc. https://doi.org/10.1016/B978-0-12-437552-9.X5000-4 | |
dc.relation | Stanford Center for Reproducible Neuroscience. (2021, December). https://openneuro.org/search/modality/eeg. | |
dc.relation | Subasi, A., & Erçelebi, E. (2005). Classification of EEG signals using neural network and logistic regression. Computer Methods and Programs in Biomedicine, 78(2), 87–99. https://doi.org/10.1016/j.cmpb.2004.10.009 | |
dc.relation | TADDEI, A., DISTANTE, G., EMDIN, M., PISANI, P., MOODY, G. B., ZEELENBERG, C., & MARCHESI, C. (1992). The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. European Heart Journal, 13(9). https://doi.org/10.1093/oxfordjournals.eurheartj.a060332 | |
dc.relation | Tang, Y.-Y., Hölzel, B. K., & Posner, M. I. (2015). The neuroscience of mindfulness meditation. Nature Reviews Neuroscience, 16(4), 213–225. https://doi.org/10.1038/nrn3916 | |
dc.relation | Tasika, N. J., Haque, M. H., Rimo, M. B., al Haque, M., Alam, S., Tamanna, T., Rahman, M. A., & Parvez, M. Z. (2020). A Framework for Mind Wandering Detection using EEG Signals. 2020 IEEE Region 10 Symposium (TENSYMP). https://doi.org/10.1109/TENSYMP50017.2020.9230790 | |
dc.relation | Taylor, V. A., Daneault, V., Grant, J., Scavone, G., Breton, E., Roffe-vidal, S., Courtemanche, J., Lavarenne, A. S., Marrelec, G., Benali, H., & Beauregard, M. (2013). Impact of meditation training on the default mode network during a restful state. Social Cognitive and Affective Neuroscience, 8(1). https://doi.org/10.1093/scan/nsr087 | |
dc.relation | Taylor, V. A., Daneault, V., Grant, J., Scavone, G., Breton, E., Roffe-Vidal, S., Courtemanche, J., Lavarenne, A. S., Marrelec, G., Benali, H., & Beauregard, M. (2013). Impact of meditation training on the default mode network during a restful state. Social Cognitive and Affective Neuroscience, 8(1), 4–14. https://doi.org/10.1093/scan/nsr087 | |
dc.relation | The SciPy community. (2021). SciPy. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.butter.html van Loon, R. (2018). Machine Learning Explained: Understanding Supervised, Unsupervised, and Reinforcement Learning. Data Science Central. https://www.datasciencecentral.com/profiles/blogs/machine-learning-explainedunderstanding-supervised-unsupervised | |
dc.relation | Vieira, S., Garcia-Dias, R., & Lopez Pinaya, W. H. (2020). A step-by-step tutorial on how to build a machine learning model. In Machine Learning (pp. 343–370). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00019-5 | |
dc.relation | Vieira, S., Lopez Pinaya, W. H., & Mechelli, A. (2019). Introduction to machine learning. In Machine Learning: Methods and Applications to Brain Disorders (pp. 1–20). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00001-8 | |
dc.relation | Wahlbeck, K., Anderson, P., Basu, S., McDaid, D., & Stuckler, D. (2011). Impact of economic crises on mental health. World Health, 34. http://www.euro.who.int/en/home | |
dc.relation | WHO. (2009). Global Health Risks: Mortality and burden of disease attributable to selected major risks. Bulletin of the World Health Organization, 87, 646–646. https://doi.org/10.2471/BLT.09.070565 | |
dc.relation | Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools and Techniques. In Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki (Third Edit). http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:No+Title#0 | |
dc.relation | Wood, I. K. (1996). Neuroscience: Exploring the brain. Journal of Child and Family Studies, 5(3), 377– 379. https://doi.org/10.1007/bf02234670 | |
dc.relation | World Federation For Mental Health (WFMH). (2012). Depression: A Global Crisis. World Mental Health Day, 32. http://www.who.int/mental_health/management/depression/wfmh_paper_depression_wm hd_2012.pdf. | |
dc.relation | World Health Organization. (2001). MENTAL HEALTH A Call for Action by World Health Ministers. http://www.who.int/mental_health/advocacy/en/Call_for_Action_MoH_Intro.pdf | |
dc.relation | World Health Organization. (2012). Depression, A Hidden Burden. In Fact sheet N°369. http://www.who.int/mediacentre/factsheets/fs369/en/ | |
dc.relation | World Health Organization. (2015). Fact sheet N°369 WHO Depression. World Health Organization. http://www.who.int/mental_health/management/depression/en/ | |
dc.relation | Yao, M. (2018). 6 Ways AI Transforms How We Develop Software. https://www.topbots.com/6- ways-ai-transforms-develop-software/ | |
dc.relation | Yu, X., Chum, P., & Sim, K.-B. (2014). Analysis the effect of PCA for feature reduction in nonstationary EEG based motor imagery of BCI system. Optik, 125(3), 1498–1502. https://doi.org/10.1016/j.ijleo.2013.09.013 | |
dc.relation | Zainuddin, Z., Huong, L. K., & Pauline, O. (2012). On the Use of Wavelet Neural Networks in the Task of Epileptic Seizure Detection from Electroencephalography Signals. Procedia Computer Science, 11, 149–159. https://doi.org/10.1016/j.procs.2012.09.016 | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | http://purl.org/coar/access_right/c_abf2 | |
dc.subject | Inteligencia artificial | |
dc.subject | Aprendizaje automático | |
dc.subject | Procesamiento de señales | |
dc.subject | Neurociencia ingeniería de software | |
dc.subject | Cerebro | |
dc.subject | Meditación | |
dc.subject | Mente divagante | |
dc.subject | Programa de ordenador | |
dc.subject | Inteligencia artificial | |
dc.title | Aplicación de la inteligencia computacional en el análisis de datos de electroencefalografía para la clasificación y reconocimiento de estados mentales relacionados con mente divagante y atención plena | |
dc.type | Trabajo de grado - Maestría | |
dc.type | http://purl.org/coar/resource_type/c_bdcc | |
dc.type | Text | |
dc.type | info:eu-repo/semantics/masterThesis | |
dc.type | info:eu-repo/semantics/publishedVersion |