Feature representation frameworks for decoding brain motor imagery patterns

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dc.contributor.advisorCastellanos Domínguez, César Germán
dc.contributor.advisorVelásquez Martínez, Luisa Fernanda
dc.contributor.authorZapata Castaño, Frank Yesid
dc.contributor.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000138411spa
dc.contributor.googlescholarhttps://scholar.google.com/citations?user=oNUg-cIAAAAJ&hl=es&authuser=1spa
dc.contributor.orcidZapata Castaño, Frank Yesid [0000-0003-2214-3355]spa
dc.contributor.researchgatehttps://www.researchgate.net/profile/F-Zapata-Castanospa
dc.contributor.researchgroupGrupo de Control y Procesamiento Digital de Señalesspa
dc.date.accessioned2023-06-28T20:39:02Z
dc.date.available2023-06-28T20:39:02Z
dc.date.issued2023
dc.descriptiongraficas, tablasspa
dc.description.abstractThe EEG recording records the electrical activity of the brain and measures activity with electrodes on the scalp; This record is the most widely used in the clinical and research fields due to its low cost and high temporal resolution. Due to the good temporal reso lution, Brain-Computer Interfaces (BCI) have been used frequently as a tool to decode brain activity and convert it into commands or instructions that other devices can under stand. The most well-known BCI systems are based on the Motor Imagery(MI) paradigm that corresponds to the imagination of a motor action without execution. That takes ad vantage of the synchronization dynamics of the brain. Event-Related Desynchronization and Synchronization show the channel-wise temporal dynamics related to motor activity. However, ERD/S it shows the temporal dynamics of the MI task, which presents per chan nel as the average energy relative to a segment to identify the onset of an MI intention, demands the application of a large bank of narrowband filters to find dynamic changes, and the assumption of temporal alignment ignores the between-trial temporal variations of neuronal activity. In this work, we present a signal filtering analysis based on the extraction of spatial, spectral, and temporal features that decode brain dynamics in the MI paradigm, such as the estimation of Supervised Temporal Patterns (STP) resulting from the solution of a problem. of generalized eigenvalues, taking into account temporal variations in brain ac tivity. In addition, the signal filtering analysis detects the temporal dynamics, in another case the result of Functional Connectivity (wPLI) is presented to see the spatial relation ship, through frequency and time, as happens with the detailed analysis presented by the ERD/S that is also related to MI tasks in response induced within each trial. Finally, with the extraction of the spatial, frequency, and temporal pattern, the group analysis is carried out, which allows recognition of the common activity, about the neuronal response in the presence of a stimulus, helping in the process of recognizing relevant information. (Texto tomado de la fuente)eng
dc.description.abstractEl Electroencefalograma (EEG) es un registro con el cual se mide la actividad eléctrica del cerebro, se lleva a cabo mediante electrodos ubicados sobre el cuero cabelludo; este registro es el más utilizado en el campo clínico y de investigación debido a su bajo costo y alta resolución temporal. Debido a esto, el EEG ha sido ampliamente utilizado en las Interfaces Cerebro-Computadora (ICC) como una herramienta para decodificar la actividad cerebral y convertirla en comandos o instrucciones que otros dispositivos puedan entender. Los sistemas BCI más conocidos se basan en el paradigma Imaginación Motora (IM) que corresponde a la imaginación de una acción motora sin ejecución. Eso aprovecha la dinámica de sincronización del cerebro. La Desincronización y Sincronización Relacionadas con Eventos muestran la dinámica temporal a nivel de canal relacionada con la actividad motora. Sin embargo, ERD/S exige la aplicación de un gran banco de filtros de banda estrecha para encontrar cambios dinámicos, y la suposición de alineación temporal ignora las variaciones temporales entre ensayos de la actividad neuronal. En este trabajo, presentamos un análisis de filtrado de señales basado en la extracción de características espaciales, espectrales y temporales que decodifican la dinámica cerebral en el paradigma MI, como la estimación de Patrones Temporales Supervisados (PTS) que resulta de la solución de un problema de autovalores generalizados, teniendo en cuenta las variaciones temporales de la actividad cerebral. Además, el análisis de filtrado de señales detecta la dinámica temporal, en otro caso se presenta el resultado de Conectividad Funcional (wPLI) para ver relación espacial, a través de la frecuencia y el tiempo, como también sucede con el análisis detallado que presenta el ERD/S que es relacionada con las tareas de MI en respuesta inducida dentro de cada ensayo. Finalmente con la extracción del patrón espacial, frecuencial y temporal, se lleva al análisis de grupo que permite reconocer la actividad común, en referencia con la respuesta neuronal en presencia de un estímulo, ayudando en el proceso de reconocer información relevante.spa
dc.description.curricularareaEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizalesspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Automatización Industrialspa
dc.description.researchareaAutomatización Industrial con enfoque en tratamiento de bioseñales (EEG)spa
dc.description.technicalinfoCodes: Presentation of algorithms developed in the different works of this work. Repositorio Github: - Chapter 1: https://github.com/FrankYesid/Supervised-Temporal-Patterns-STP - Chapter 2: https://github.com/FrankYesid/MaskERDs - Chapter 3: https://github.com/FrankYesid/Group_analysis_EEG - Chapter Future work: https://github.com/FrankYesid/Music-EEG-Activityeng
dc.format.extentxii, 62 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84097
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Automatización Industrialspa
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generalesspa
dc.subject.proposalActividad cerebralspa
dc.subject.proposalAnálisis de relevancia de característicasspa
dc.subject.proposalCaracterísticas de tiempo cortospa
dc.subject.proposalElectroencefalografíaspa
dc.subject.proposalImaginación motoraspa
dc.subject.proposalInterfaz hombre máquinaspa
dc.subject.proposalCaracterísticas de señales de electroencefalografíaspa
dc.subject.proposalElectroencephalography (EEG) signaleng
dc.subject.proposalNeural activityeng
dc.subject.proposalBrain-machine interfaceeng
dc.subject.proposalMotor imageryeng
dc.subject.proposalPreprocessingeng
dc.subject.proposalShort-time featureseng
dc.subject.proposalFeature relevance analysiseng
dc.subject.proposalEEG Signal Featureseng
dc.subject.unescoTecnología médicaspa
dc.subject.unescoMedical technologyeng
dc.titleFeature representation frameworks for decoding brain motor imagery patternseng
dc.title.translatedRepresentación de características para decodificar patrones de imágenes motoras cerebraleseng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentImagespa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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