Colombia | Trabajo de grado - Maestría
dc.contributorCastillo Ossa, Luis Fernando
dc.contributorGustavo Isaza
dc.contributorCarlos Ferrin
dc.contributorJeferson Arango
dc.contributorGITIR Grupo de Investigación en Tecnologías de la Información y Redes (Categoría A)
dc.creatorMontes Marín, Leonardo
dc.date2022-05-02T22:49:40Z
dc.date2022-05-02T22:49:40Z
dc.date2022-04-28
dc.date.accessioned2023-09-06T18:31:11Z
dc.date.available2023-09-06T18:31:11Z
dc.identifierhttps://repositorio.ucaldas.edu.co/handle/ucaldas/17585
dc.identifierUniversidad de Caldas
dc.identifierRepositorio Institucional Universidad de Caldas
dc.identifierhttps://repositorio.ucaldas.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8698618
dc.descriptionIlustraciones
dc.descriptionspa: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.descriptioneng: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.descriptionTítulo 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.descriptionMaestría
dc.descriptionMagister en Ingeniería Computacional
dc.descriptionModelos Biocomputacionales y Bioinformática
dc.descriptionInteligencia Computacional y Organizacional
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.languagespa
dc.publisherFacultad de Ingeniería
dc.publisherManizales
dc.publisherMaestría en Ingeniería Computacional
dc.relationa, 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.relationAbercrombie, 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.relationAcı, 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.relationAdomavicius, 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.relationAliño, 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.relationAmerican 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.relationAnand, 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.relationAnand, 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.relationAnderson, 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.relationAura Health. (2017). Aura: Best Mindfulness Meditation App for Stress and Anxiety. https://www.aurahealth.io/
dc.relationAustin, J. H. (2006). Zen-Brain Reflections. http://www.elibrary.ibc.ac.th/files/private/Zen-Brain Reflections.pdf
dc.relationBaer, 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.relationBao, 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.relationBarachant, 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.relationBarachant, 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.relationBarascud Nicolas. (2021). MEEGkit: EEG and MEG denoising in Python. https://nbara.github.io/python-meegkit/modules/meegkit.asr.html
dc.relationBarlow John. (1993). The Electroencephalogram. Its Patterns and Origins. The MIT Press. https://mitpress.mit.edu/books/electroencephalogram
dc.relationBeninger, 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.relationBerkovich-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.relationBhuvaneswari, 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.relationBishop, 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.relationBlum, 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.relationBrandmeyer, 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.relationBrefczynski-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.relationBrefczynski-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.relationBrefczynski-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.relationBrown, K. W., & Ryan, R. M. (n.d.). The benefits of being present: Mindfulness and its role in psychological well-being.
dc.relationBuckner, 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.relationCabañero, 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.relationCao, 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.relationCarmody, 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.relationCenter for Investigating Healthy Minds. (2015). Center for Investigating Healthy Minds - Center Founder, Dr. Richard J. Davidson. http://www.investigatinghealthyminds.org/cihmDrDavidson.html
dc.relationCenter for Open Science. (2021). OSF Center for Open Science. Https://Osf.Io/. https://osf.io/
dc.relationChang, 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.relationChen, 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.relationChen, 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.relationChen, 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.relationChen, 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.relationChiesa, 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.relationChristoff, 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.relationCombrisson, 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.relationCraven W. Mark, W. S. J. (n.d.). Using Neuronal Networks for Data Mining. 01
dc.relationDehuri, 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.relationDelorme, 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.relationDemšar J. (2006). Statistical Comparisons of Classifiers over Multiple Data Sets. The Journal of Machine Learning Research, 7, 1–30.
dc.relationDong, 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.relationEkman, 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.relationEkman, 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.relationElahi, 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.relationFarb, 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.relationFawcett, 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.relationFerrin 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.relationFingelkurts, 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.relationFlook, 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.relationFomina, 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.relationGarrison, 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.relationGasser, 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.relationGennady 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.relationGhassemi, 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.relationGoldin, 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.relationGoleman, D., & Davidson, R. (2017). Altered Traits: Science Reveals How Meditation Changes Your Mind, Brain, and Body.
dc.relationGotink, 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.relationGoyal, 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.relationGramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7. https://doi.org/10.3389/fnins.2013.00267
dc.relationGrandchamp, R., Braboszcz, C., & Delorme, A. (2014). Oculometric variations during mind wandering. Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00031
dc.relationGranville, 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.relationGrégoire Cattan, Pedro Rodrigues, & Marco Congedo. (2018). EEG Alpha Waves dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.2348892
dc.relationGroot, 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.relationHanke, 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.relationHarris, 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.relationHastie, 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.relationHjorth, 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.relationHosseini, 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.relationIchimaru, 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.relationIrrmischer, 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.relationJang, 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.relationJang, 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.relationJEN L. TEE, & WAI Y. LEONG. (2018). EEG extraction for meditation. Journal of Engineering Science and Technology, 13(7), 2125 – 2135.
dc.relationJia, 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.relationJin 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.relationJin, 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.relationJin, 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.relationKabat-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.relationkaggle. (2019). Meditation-EEG-Data. https://www.kaggle.com/abyssjumper/meditationeegdata/code
dc.relationKaggle. (2021). Kaggle: Your Machine Learning and Data Science Community. https://www.kaggle.com/
dc.relationKaur, 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.relationKawashima, 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.relationKim, 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.relationKim, 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.relationKim, 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.relationKim, 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.relationKirk, 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.relationKnyazev, 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.relationKoles, 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.relationKoszycki, 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.relationKothe, 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.relationKuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer New York. https://doi.org/10.1007/978-1-4614-6849-3
dc.relationLahane, 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.relationLawhern, 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.relationLin, H., & Li, Y. (2017). Using EEG Data Analytics to Measure Meditation. https://doi.org/10.1007/978-3-319-58466-9_25
dc.relationLomas, 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.relationLomas, 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.relationLomas, 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.relationLotte, 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.relationLotte, 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.relationLutz, 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.relationLutz, 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.relationLutz, 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.relationMaini, 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.relationManiar, S., Kalra, R., Shinaman, R., & Longton William. (2017). WellBrain. http://wellbrain.io/
dc.relationManuello, 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.relationMathworks. (2017). Machine Learning with MATLAB. https://la.mathworks.com/campaigns/offers/machine-learning-withmatlab.html?s_tid=hp_offer_ml_ebok
dc.relationMayberg, 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.relationMcBride, 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.relationMcKenna, 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.relationMcKinney, W. (2010). Data Structures for Statistical Computing in Python. 56–61. https://doi.org/10.25080/Majora-92bf1922-00a
dc.relationMitchell, 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.relationMohammadi, 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.relationMoody, 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.relationMullen, 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.relationNeuner, 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.relationNeuralink. (n.d.). Home - Neuralink. Retrieved May 23, 2021, from https://neuralink.com/
dc.relationOken, 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.relationOrhan, 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.relationOthman, 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.relationParvinnia, 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.relationPedregosa, 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.relationPhysioNet. (n.d.). AHA Database Sample Excluded Record. Retrieved May 17, 2021, from https://physionet.org/content/ahadb/1.0.0/
dc.relationPizzagalli, 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.relationPrerau, 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.relationRaichle, M. E. (2015). The Brain’s Default Mode Network. Annual Review of Neuroscience. https://doi.org/10.1146/annurev-neuro-071013-014030
dc.relationRechy-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.relationRivet, 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.relationRodriguez‐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.relationSaha, 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.relationScheeringa, 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.relationShams, 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.relationShneiderman, B., & Plaisant, C. (2009). Designing the User Interface: Strategies for Effective HumanComputer Interaction. Nature Publishing Group.
dc.relationShoemaker, A. (2014). Ayahuasca Medicine: The Shamanic World of Amazonian Sacred Plant Healing. https://doi.org/9781620551936
dc.relationSilicon 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.relationSimon, 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.relationSmallwood, 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.relationSörnmo, 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.relationStanford Center for Reproducible Neuroscience. (2021, December). https://openneuro.org/search/modality/eeg.
dc.relationSubasi, 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.relationTADDEI, 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.relationTang, 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.relationTasika, 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.relationTaylor, 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.relationTaylor, 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.relationThe 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.relationVieira, 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.relationVieira, 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.relationWahlbeck, 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.relationWHO. (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.relationWitten, 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.relationWood, I. K. (1996). Neuroscience: Exploring the brain. Journal of Child and Family Studies, 5(3), 377– 379. https://doi.org/10.1007/bf02234670
dc.relationWorld 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.relationWorld 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.relationWorld Health Organization. (2012). Depression, A Hidden Burden. In Fact sheet N°369. http://www.who.int/mediacentre/factsheets/fs369/en/
dc.relationWorld Health Organization. (2015). Fact sheet N°369 WHO Depression. World Health Organization. http://www.who.int/mental_health/management/depression/en/
dc.relationYao, M. (2018). 6 Ways AI Transforms How We Develop Software. https://www.topbots.com/6- ways-ai-transforms-develop-software/
dc.relationYu, 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.relationZainuddin, 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.rightsinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectInteligencia artificial
dc.subjectAprendizaje automático
dc.subjectProcesamiento de señales
dc.subjectNeurociencia ingeniería de software
dc.subjectCerebro
dc.subjectMeditación
dc.subjectMente divagante
dc.subjectPrograma de ordenador
dc.subjectInteligencia artificial
dc.titleAplicación 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.typeTrabajo de grado - Maestría
dc.typehttp://purl.org/coar/resource_type/c_bdcc
dc.typeText
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typeinfo:eu-repo/semantics/publishedVersion


Este ítem pertenece a la siguiente institución