Deep learning framework with enhanced interpretability for classification of motor imagery tasks

graficas, tablas

Autores:: Collazos Huertas, Diego Fabian

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

id	UNACIONAL2_6a349f5fc4916e8c0422aab6cf5f4315
oai_identifier_str	oai:repositorio.unal.edu.co:unal/84594
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.eng.fl_str_mv	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
dc.title.translated.spa.fl_str_mv	Marco de aprendizaje profundo con interpretabilidad mejorada para la clasificación de tareas de imaginación motora
title	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
spellingShingle	Deep learning framework with enhanced interpretability for classification of motor imagery tasks 620 - Ingeniería y operaciones afines Deep learning EEG Motor imagery Deep&Wide network Transfer learning Physiological interpretability Aprendizaje profundo Imaginación motora Aprendizaje por transferencia Interpretabilidad fisiológica Tecnología médica Ingeniería Medical technology Engineering
title_short	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
title_full	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
title_fullStr	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
title_full_unstemmed	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
title_sort	Deep learning framework with enhanced interpretability for classification of motor imagery tasks
dc.creator.fl_str_mv	Collazos Huertas, Diego Fabian
dc.contributor.advisor.none.fl_str_mv	Castellanos-Dominguez, German
dc.contributor.author.none.fl_str_mv	Collazos Huertas, Diego Fabian
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Control y Procesamiento Digital de Señales
dc.contributor.orcid.spa.fl_str_mv	Collazos Huertas, Diego Fabian [0002-0434-3444]
dc.contributor.cvlac.spa.fl_str_mv	Collazos Huertas, Diego Fabian [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000017335]
dc.contributor.researchgate.spa.fl_str_mv	Collazos Huertas, Diego Fabian [https://www.researchgate.net/profile/Diego-Collazos]
dc.contributor.googlescholar.spa.fl_str_mv	Collazos Huertas, Diego Fabian [D.F Collazos-Huertas]
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines
topic	620 - Ingeniería y operaciones afines Deep learning EEG Motor imagery Deep&Wide network Transfer learning Physiological interpretability Aprendizaje profundo Imaginación motora Aprendizaje por transferencia Interpretabilidad fisiológica Tecnología médica Ingeniería Medical technology Engineering
dc.subject.proposal.eng.fl_str_mv	Deep learning EEG Motor imagery Deep&Wide network Transfer learning Physiological interpretability
dc.subject.proposal.spa.fl_str_mv	Aprendizaje profundo Imaginación motora Aprendizaje por transferencia Interpretabilidad fisiológica
dc.subject.unesco.spa.fl_str_mv	Tecnología médica Ingeniería
dc.subject.unesco.eng.fl_str_mv	Medical technology Engineering
description	graficas, tablas
publishDate	2022
dc.date.issued.none.fl_str_mv	2022
dc.date.accessioned.none.fl_str_mv	2023-08-23T20:54:23Z
dc.date.available.none.fl_str_mv	2023-08-23T20:54:23Z
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Image Text
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/84594
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84594 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
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dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Manizales
institution	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos-Dominguez, Germane7877f60c5ac464594daa00d2d4e8180600Collazos Huertas, Diego Fabian5cc69bc03905da42acdf3868d78f9c56600Grupo de Control y Procesamiento Digital de SeñalesCollazos Huertas, Diego Fabian [0002-0434-3444]Collazos Huertas, Diego Fabian [https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000017335]Collazos Huertas, Diego Fabian [https://www.researchgate.net/profile/Diego-Collazos]Collazos Huertas, Diego Fabian [D.F Collazos-Huertas]2023-08-23T20:54:23Z2023-08-23T20:54:23Z2022https://repositorio.unal.edu.co/handle/unal/84594Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasDeep learning (DL) allows models composed of multiple processing layers to learn representations of data with several levels of abstraction. These methods have improved state-of-the-art tasks like speech recognition, visual object identification, and many other fields. Regarding electroencephalographic (EEG) signals analysis, especially for the Motor Imagery (MI) paradigm, the availability of large data sets and advances in machine learning have led to the deployment of DL architectures, allowing the understanding of the information that may contain for brain functionality. However, these models suffer some limitations in practice: i) often DL models not integrate properly EEG spatial information with extracted time-frequency features, ii) the resulting inter and intra-subject variability, along with frequently available small datasets, significantly decreases the performance of EEG-based MI systems, and iii) DL models are treated as “black boxes” lacking physiological interpretability. In this Ph.D. thesis proposal, we pretend to solve these issues i) developing a Deep&Wide learning methodology using multi-view feature extraction, ii) proposing a coupling information strategy based on transfer learning including subject’s clinical data, and iii) developing a relevance analysis methodology that allows improving the interpretability of neural responses. The detailed methodology and its respective execution plan (schedule) to carry out these objectives are further described. In addition, we list the available computational resources necessary for the proposed implementation (Texto tomado de la fuente)El aprendizaje profundo (por sus siglas en inglés DL) permite que los modelos compuestos por múltiples capas de procesamiento aprendan representaciones de datos con varios niveles de abstracción. Estos métodos han mejorado tareas de vanguardia como el reconocimiento de voz, la identificación de objetos visuales y muchos otros campos. En cuanto al análisis de señales electroencefalográficas (EEG), especialmente para el paradigma de Imaginación Motora (por sus siglas en inglés MI), la disponibilidad de grandes conjuntos de datos y los avances en el aprendizaje automático han llevado al despliegue de arquitecturas DL, permitiendo la comprensión de la información que puede contener para la funcionalidad cerebral. Sin embargo, estos modelos sufren algunas limitaciones en la práctica: i) a menudo los modelos DL no integran correctamente la información espacial de EEG con las características extraídas de tiempo-frecuencia, ii) la alta variabilidad inter e intra-sujeto resultante, junto con los pequeños conjuntos de datos disponibles, disminuye significativamente el rendimiento de los sistemas MI a partir de registros EEG, y iii) los modelos DL se tratan como “cajas negras ” que carecen de interpretabilidad fisiológica. En esta propuesta de tesis, pretendemos resolver estos problemas i) desarrollando una metodolog´ıa de aprendizaje Deep&Wide utilizando extracción de características de múltiples dominios, ii) proponiendo una estrategia de acoplamiento de información basada en el aprendizaje de transferencia que incluye los datos clínicos del sujeto, y iii) desarrollando una metodología de análisis de relevancia que permita mejorar la interpretabilidad de las respuestas neuronales. La metodología detallada y su respectivo plan de ejecución (cronograma) para llevar a cabo estos objetivos de describe más adelante. Además, se reportan los recursos computacionales disponibles y necesarios para la implementación de esta propuesta.Minciencias a través de la convocatoria Doctorados Nacionales Conv. 785 -2017DoctoradoDoctor en IngenieríaInteligencia artificial y Machine LearningEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesxxii, 127 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales620 - Ingeniería y operaciones afinesDeep learningEEGMotor imageryDeep&Wide networkTransfer learningPhysiological interpretabilityAprendizaje profundoImaginación motoraAprendizaje por transferenciaInterpretabilidad fisiológicaTecnología médicaIngenieríaMedical technologyEngineeringDeep learning framework with enhanced interpretability for classification of motor imagery tasksMarco de aprendizaje profundo con interpretabilidad mejorada para la clasificación de tareas de imaginación motoraTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06ImageText[Aellen et al., 2021] Aellen, F., G¨oktepe-Kavis, P., Apostolopoulos, S., and Tzovara, A. (2021). Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features. Journal of Neuroscience Methods, 364:109367.[Aggarwal and Chugh, 2019] Aggarwal, S. and Chugh, N. (2019). Signal processing techniques for motor imagery brain computer interface: A review. Array, 1:100003.[Al-Saegh et al., 2021] Al-Saegh, A., Dawwd, S., and Abdul-Jabbar, J. (2021). Deep learning for motor imagery eeg-based classification: A review. Biomedical Signal Processing and Control, 63:102172.[Alonso-Valerdi, 2016] Alonso-Valerdi, L. (2016). Python executable script for estimating two effective parameters to individualize brain-computer interfaces: Individual alpha frequency and neurophysiological predictor. Frontiers in neuroinformatics, 10:22.Altaheri et al., 2021] Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S., Altuwaijri, G., Abdul, W., Bencherif, M., and Faisal, M. (2021). 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Journal of neuroengineering and rehabilitation, 5(1):1–10.MincienciasBibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalORIGINAL1053812740.2022.pdf1053812740.2022.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf18769057https://repositorio.unal.edu.co/bitstream/unal/84594/2/1053812740.2022.pdf571bc16fb2be9497e21fc0e273a8a11aMD52THUMBNAIL1053812740.2022.pdf.jpg1053812740.2022.pdf.jpgGenerated Thumbnailimage/jpeg4544https://repositorio.unal.edu.co/bitstream/unal/84594/3/1053812740.2022.pdf.jpgcedcc4542db6f6f0e283b90a2ee62a7cMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84594/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51unal/84594oai:repositorio.unal.edu.co:unal/845942023-08-23 23:03:48.369Repositorio Institucional Universidad Nacional de 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Deep learning framework with enhanced interpretability for classification of motor imagery tasks

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