Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints

graficas, tablas

Autores:: Velásquez Martínez, Luisa Fernanda

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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network_acronym_str	UNACIONAL2
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repository_id_str
dc.title.eng.fl_str_mv	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
dc.title.translated.spa.fl_str_mv	Discriminación de estados mentales mediante la extracción de patrones espaciales bajo restricciones de no estacionariedad e independencia de sujeto
title	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
spellingShingle	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints 000 - Ciencias de la computación, información y obras generales Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
title_short	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_full	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_fullStr	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_full_unstemmed	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
title_sort	Discriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraints
dc.creator.fl_str_mv	Velásquez Martínez, Luisa Fernanda
dc.contributor.advisor.none.fl_str_mv	Castellanos Dominguez, Cesar Germán
dc.contributor.author.none.fl_str_mv	Velásquez Martínez, Luisa Fernanda
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Control y Procesamiento Digital de Señales
dc.contributor.googlescholar.spa.fl_str_mv	https://scholar.google.com.co/citations?user=kzOD4RQAAAAJ&hl=en
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
dc.subject.proposal.eng.fl_str_mv	Motor Imagery Event-related synchronization Entropy Brain-computer interface BCI inefficiency Regression networks Multi-subject analysis
dc.subject.proposal.spa.fl_str_mv	Imaginación Motora Sincronización relacionada a eventos Entropia Interfaces cerebro computador Ineficiencia en BCI Redes de regresión Análisis multi-sujeto
description	graficas, tablas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-08-14T20:31:52Z
dc.date.available.none.fl_str_mv	2023-08-14T20:31:52Z
dc.date.issued.none.fl_str_mv	2023
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
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
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/84556
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/84556 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
dc.publisher.program.spa.fl_str_mv	Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Automática
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería y Arquitectura
dc.publisher.place.spa.fl_str_mv	Manizales, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Manizales
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Dominguez, Cesar Germán954ae54839a598cda0bf4ca7d98773d9Velásquez Martínez, Luisa Fernanda170534193d1c583d3337a67272e54ea9Grupo de Control y Procesamiento Digital de Señaleshttps://scholar.google.com.co/citations?user=kzOD4RQAAAAJ&hl=en2023-08-14T20:31:52Z2023-08-14T20:31:52Z2023https://repositorio.unal.edu.co/handle/unal/84556Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/graficas, tablasEvaluation of brain dynamics elicited by motor imagery (MI) tasks can contribute to clinical and learning applications. In this work, we propose four specific improvements for brain motor intention response analysis based on EEG recordings by considering the nonstationarity, nonlinearity of brain signals, inter- and intra-subject variability, aimed to provide physiological interpretability and the distintiveness between subjects neural response. Firstly, to build up the subject-level feature framework, a common representational space, is proposed that encodes the electrode (spatial) contribution, evolving through time and frequency domains. Three feature extraction methods were compared, providing insight into the possible limitations. Secondly, we present an Entropy-based method, termed \textit{VQEnt}, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that \textit{VQEnt} holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the \textit{VQEnt} estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set. Thirdly, multi-subject analysis is to make inferences on the group/population level about the properties of MI brain activity. However, intrinsic neurophysiological variability of neural dynamics poses a challenge for devising efficient MI systems. Here, we develop a \textit{time-frequency} model for estimating the spatial relevance of common neural activity across subjects employing an introduced statistical thresholding rule. In deriving multi-subject spatial maps, we present a comparative analysis of three feature extraction methods: \textit{Common Spatial Patterns}, \textit{Functional Connectivity}, and \textit{Event-Related De/Synchronization}. In terms of interpretability, we evaluate the effectiveness in gathering MI data from collective populations by introducing two assumptions: \textit{i}) Non-linear assessment of the similarity between multi-subject data originating the subject-level dynamics; \textit{ii}) Assessment of time-varying brain network responses according to the ranking of individual accuracy performed in distinguishing distinct motor imagery tasks (left-hand versus right-hand). The obtained validation results indicate that the estimated collective dynamics differently reflect the flow of sensorimotor cortex activation, providing new insights into the evolution of MI responses. Lastly, we develop a data-driven estimator, termed {Deep Regression Network} (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain--Computer Interface inefficiency of subjects. (Texto tomado de la fuente)La evaluación de la dinámica cerebral provocada por las tareas de imaginación motora (\textit{Motor Imagery - MI}) puede contribuir al desarrollo de aplicaciones clínicas y de aprendizaje. En este trabajo, se proponen cuatro mejoras específicas para el an\'lisis de la respuesta de la intención motora cerebral basada en registros de Electroencefalografía (EEG) al considerar la no estacionariedad, la no linealidad de las se\tilde{n}ales cerebrales y la variabilidad inter e intrasujeto, con el objetivo de proporcionar interpretabilidad fisiológica y la discriminación entre la respuesta neuronal de los sujetos. En primer lugar, para construir el marco de características a nivel de sujeto, se propone un espacio de representación común que codifica la contribución del electrodo (espacial) y como esta evoluciona a través de los dominios de tiempo y frecuencia. Tres métodos de extracción de características fueron comparados, proporcionando información sobre las posibles limitaciones. En segundo lugar, se presenta un método basado en Entropía, denominado \textit{VQEnt}, para la estimación de la desincronización relacionada a eventos (\textit{Event-Related De-Synchronization - ERD/S}) utilizando patrones estocásticos cuantificados en un espacio simbólico, con el objetivo de mejorar su discriminabilidad e interpretabilidad fisiol\'gica. El método propuesto construye los antecedentes probabilísticos mediante la evaluación de la similitud gaussiana entre los datos medidos de entrada y su representación cuantificada vectorial reducida. Los resultados de validación en una base de datos de tareas de imaginación bi-clase (mano izquierda y mano derecha) prueban que \textit{VQEnt} contiene símbolos que codifican varias muestras vecinas, proporcionando una precisión similar o incluso mejor que los otros algoritmos basados en estimación de entropía de referencia. Además, las series temporales de ERD/S calculadas son lo suficientemente cercanas a las trayectorias extraídas por el porcentaje de variación de la potencia de la señal EEG y cumplen con el paradigma fisiológico de MI. En individuos alfabetizados en BCI, el estimador \textit{VQEnt} presenta los resultados precisos con una menor cantidad de electrodos colocados en la corteza sensoriomotora, de modo que el conjunto reducido de canales directamente involucrados con el paradigma MI es suficiente para discriminar entre tareas. En tercer lugar, el análisis multisujeto consiste en hacer inferencias a nivel de grupo/población sobre las propiedades de la actividad cerebral de la imaginación motora. Sin embargo, la variabilidad neurofisiológica intrínseca de la dinámica neuronal plantea un desafío para el diseño de sistemas MI eficientes. En este sentido, se presenta un modelo de \textit{tiempo-frecuencia} para estimar la relevancia espacial de la actividad neuronal común entre sujetos empleando una regla de umbral estadística que deriva en mapas espaciales de múltiples sujetos. Se presenta un análisis comparativo de tres métodos de extracción de características: \textit{Patrones espaciales comunes}, \textit{Conectividad funcional} y \textit{De-sincronización relacionada con eventos}. En términos de interpretabilidad, evaluamos la efectividad en la recopilación de datos de MI para multisujetos mediante la introducción de dos suposiciones: \textit{i}) Evaluación no lineal de la similitud entre los datos de múltiples sujetos que originan la dinámica a nivel de sujeto; \textit{ii}) Evaluación de las respuestas de la red cerebral que varían en el tiempo de acuerdo con la clasificación de la precisión individual realizada al distinguir distintas tareas de imaginación motora (mano izquierda versus mano derecha). Los resultados de validación obtenidos indican que la dinámica colectiva estimada refleja de manera diferente el flujo de activación de la corteza sensoriomotora, lo que proporciona nuevos conocimientos sobre la evolución de las respuestas de MI. Por último, se muestra un estimador denominado {Red de regresión profunda} (\textit{Deep Regression Network - DRN}), que extrae y realiza conjuntamente un análisis de regresión para evaluar la eficiencia de las redes cerebrales individuales, de cada sujeto, en la práctica de tareas de MI. El estimador de doble etapa propuesto inicialmente aprende un conjunto de patrones profundos, extraídos de los datos de entrada, para alimentar un modelo de regresión neuronal, lo que permite inferir la distinción entre conjuntos de sujetos que tienen una variabilidad similar. Los resultados, que se obtuvieron con datos MI del mundo real, demuestran que el estimador DRN usa la desincronización neuronal previa al entrenamiento y la sincronización del entrenamiento inicial para predecir la respuesta de precisión bi-clase, proporcionando así una mejor comprensión de la ineficiencia de la respuesta de MI de los sujetos en las Interfaces Cerebro-Computador.DoctoradoDoctor en IngenieríaReconocimiento de PatronesEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesxii, 77 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - AutomáticaFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales000 - Ciencias de la computación, información y obras generalesMotor ImageryEvent-related synchronizationEntropyBrain-computer interfaceBCI inefficiencyRegression networksMulti-subject analysisImaginación MotoraSincronización relacionada a eventosEntropiaInterfaces cerebro computadorIneficiencia en BCIRedes de regresiónAnálisis multi-sujetoDiscriminating mental states by extracting relevant spatial patterns under non-stationary and subject-independent constraintsDiscriminación de estados mentales mediante la extracción de patrones espaciales bajo restricciones de no estacionariedad e independencia de sujetoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06ImageTexthttp://purl.org/redcol/resource_type/TDB. 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Zhu, "Fast recognition of bci-ine cient users using physiological features from eeg signals: A screening study of stroke patients," Frontiers in neuroscience, vol. 12, p. 93, 2018.MincienciasBibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84556/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1053768739.2023.pdf1053768739.2023.pdfTesis de Doctorado en Ingeniería - Automáticaapplication/pdf27115133https://repositorio.unal.edu.co/bitstream/unal/84556/5/1053768739.2023.pdff3a5715dc69cb0c853062634007f362cMD55THUMBNAIL1053768739.2023.pdf.jpg1053768739.2023.pdf.jpgGenerated Thumbnailimage/jpeg5351https://repositorio.unal.edu.co/bitstream/unal/84556/6/1053768739.2023.pdf.jpgfb6f0bd9f5e095193b8489fb94a2281dMD56unal/84556oai:repositorio.unal.edu.co:unal/845562023-08-15 23:04:09.346Repositorio Institucional Universidad Nacional de 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