Brain connectivity-patterns representation based on electroencephalography network analysis
Brain connectivity has emerged as a neuronal analysis tool widely used to explore brain functions and supply relevant information in the study of the cognitive processes. However, current methodologies used to assess brain connectivity are not always exact and as a result, possible spurious connecti...
- Autores:
-
Hurtado Rincón, Juana Valeria
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/64168
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/64168
http://bdigital.unal.edu.co/65000/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Brain connectivity
Electroencephalography
Significant connections
sdffsdf
Conectividad cerebral
Electroencefalografía
Conexiones significativas
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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|
dc.title.spa.fl_str_mv |
Brain connectivity-patterns representation based on electroencephalography network analysis |
title |
Brain connectivity-patterns representation based on electroencephalography network analysis |
spellingShingle |
Brain connectivity-patterns representation based on electroencephalography network analysis 62 Ingeniería y operaciones afines / Engineering Brain connectivity Electroencephalography Significant connections sdffsdf Conectividad cerebral Electroencefalografía Conexiones significativas |
title_short |
Brain connectivity-patterns representation based on electroencephalography network analysis |
title_full |
Brain connectivity-patterns representation based on electroencephalography network analysis |
title_fullStr |
Brain connectivity-patterns representation based on electroencephalography network analysis |
title_full_unstemmed |
Brain connectivity-patterns representation based on electroencephalography network analysis |
title_sort |
Brain connectivity-patterns representation based on electroencephalography network analysis |
dc.creator.fl_str_mv |
Hurtado Rincón, Juana Valeria |
dc.contributor.advisor.spa.fl_str_mv |
Castellanos Domínguez, César Germán (Thesis advisor) Martínez Vargas, Juan David (Thesis advisor) |
dc.contributor.author.spa.fl_str_mv |
Hurtado Rincón, Juana Valeria |
dc.subject.ddc.spa.fl_str_mv |
62 Ingeniería y operaciones afines / Engineering |
topic |
62 Ingeniería y operaciones afines / Engineering Brain connectivity Electroencephalography Significant connections sdffsdf Conectividad cerebral Electroencefalografía Conexiones significativas |
dc.subject.proposal.spa.fl_str_mv |
Brain connectivity Electroencephalography Significant connections sdffsdf Conectividad cerebral Electroencefalografía Conexiones significativas |
description |
Brain connectivity has emerged as a neuronal analysis tool widely used to explore brain functions and supply relevant information in the study of the cognitive processes. However, current methodologies used to assess brain connectivity are not always exact and as a result, possible spurious connections may appear. Moreover, measuring the connection between all possible pairs of EEG-channels leads to high dimensional matrices with either redundant or irrelevant information. To avoid problems in connectivity analysis and issues of high computational cost, a selection stage of the most significant connections can be implemented. Nevertheless, there is not a standard method yet to extract connections and the definition of significant connections may vary accordingly with the object of study. Therefore, to develop an accurate methodology, information inherent to each specific problem should be included. In this work, three different tools are presented, that execute the extraction of significant connections considering the experimental scenario. The first tool, tested on a BCI dataset, finds the set of connections that best discriminate two MI classes. Consequently, a kernel-based methodology of feature selection is used to rank each connection by its contribution in the classes discrimination. Finally, the significant connections will be the smaller set that achieves the best classification accuracy. The second methodology is used in a study of the significant connectivity patterns in attention networks. To this end, the connectivity of two classes (target and non-target) in an oddball paradigm experiment is extracted. Here, the significant connections are selected as the ones that differ the most, statistically speaking, between target and non-target. Finally, in a study of the recovery of a subject with aphasia, differences in connectivity, related to improvements produced by therapy were found. In this study, connections that change through the sessions of treatment at the level of amplitude and structure were extracted. Also, a set of significant connections that changed increasingly between the sessions was selected. For all the proposed methodologies, the brain connectivity is computed over EEG signals and the extraction of the significant connections is based on information inherent to the data or the experiment. In general, the selection of connections allows the considerable reduction of connectivity characteristics, this facilitates the physiological interpretation of the experiments and can improve the performance and computational cost of the systems that use these features |
publishDate |
2018 |
dc.date.issued.spa.fl_str_mv |
2018 |
dc.date.accessioned.spa.fl_str_mv |
2019-07-02T22:35:19Z |
dc.date.available.spa.fl_str_mv |
2019-07-02T22:35:19Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/64168 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/65000/ |
url |
https://repositorio.unal.edu.co/handle/unal/64168 http://bdigital.unal.edu.co/65000/ |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y Computación Departamento de Ingeniería Eléctrica, Electrónica y Computación |
dc.relation.references.spa.fl_str_mv |
Hurtado Rincón, Juana Valeria (2018) Brain connectivity-patterns representation based on electroencephalography network analysis. Maestría thesis, Universidad Nacional de Colombia - Sede Manizales. |
dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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application/pdf |
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Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/bitstream/unal/64168/1/1053823047.2018.pdf https://repositorio.unal.edu.co/bitstream/unal/64168/2/1053823047.2018.pdf.jpg |
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Castellanos Domínguez, César Germán (Thesis advisor)c792a029-43aa-4eb1-ac01-0b8ac24a537e-1Martínez Vargas, Juan David (Thesis advisor)5774f532-4abc-4b2a-a9d8-e26c19fe7d73-1Hurtado Rincón, Juana Valeria69969b1b-e2f4-4dd7-b603-8de1b9e45cfe3002019-07-02T22:35:19Z2019-07-02T22:35:19Z2018https://repositorio.unal.edu.co/handle/unal/64168http://bdigital.unal.edu.co/65000/Brain connectivity has emerged as a neuronal analysis tool widely used to explore brain functions and supply relevant information in the study of the cognitive processes. However, current methodologies used to assess brain connectivity are not always exact and as a result, possible spurious connections may appear. Moreover, measuring the connection between all possible pairs of EEG-channels leads to high dimensional matrices with either redundant or irrelevant information. To avoid problems in connectivity analysis and issues of high computational cost, a selection stage of the most significant connections can be implemented. Nevertheless, there is not a standard method yet to extract connections and the definition of significant connections may vary accordingly with the object of study. Therefore, to develop an accurate methodology, information inherent to each specific problem should be included. In this work, three different tools are presented, that execute the extraction of significant connections considering the experimental scenario. The first tool, tested on a BCI dataset, finds the set of connections that best discriminate two MI classes. Consequently, a kernel-based methodology of feature selection is used to rank each connection by its contribution in the classes discrimination. Finally, the significant connections will be the smaller set that achieves the best classification accuracy. The second methodology is used in a study of the significant connectivity patterns in attention networks. To this end, the connectivity of two classes (target and non-target) in an oddball paradigm experiment is extracted. Here, the significant connections are selected as the ones that differ the most, statistically speaking, between target and non-target. Finally, in a study of the recovery of a subject with aphasia, differences in connectivity, related to improvements produced by therapy were found. In this study, connections that change through the sessions of treatment at the level of amplitude and structure were extracted. Also, a set of significant connections that changed increasingly between the sessions was selected. For all the proposed methodologies, the brain connectivity is computed over EEG signals and the extraction of the significant connections is based on information inherent to the data or the experiment. In general, the selection of connections allows the considerable reduction of connectivity characteristics, this facilitates the physiological interpretation of the experiments and can improve the performance and computational cost of the systems that use these featuresResumen: La conectividad cerebral se ha convertido en una herramienta de análisis neuronal ampliamente utilizada para explorar funciones cerebrales y proporcionar información relevante en el estudio de los procesos cognitivos. Sin embargo, las metodologías actuales utilizadas para evaluar la conectividad cerebral no siempre son exactas y, como resultado, pueden aparecer posibles conexiones falsas. Además, cuando se mide la conexión entre todos los posibles pares de canales de EEG, se obtienen matrices de alta dimensión con información redundante o irrelevante. Para evitar problemas en el análisis de conectividad y alto costo computacional, se puede implementar una etapa de selección de las conexiones más importantes. Sin embargo, todavía no existe un método estándar para extraer conexiones y la definición de conexiones significativas puede variar de acuerdo con el objeto de estudio. Por lo tanto, para desarrollar una metodología precisa, se debe incluir información inherente a cada problema. En este trabajo, se presentan tres herramientas diferentes que ejecutan una extracción de conexiones significativas considerando el escenario del experimento. La primera herramienta, probada en una base de datos de MI, encuentra el conjunto de conexiones que mejor discrimina dos clases. Para esto se utiliza una metodología de selección de características basada en kernels para asignar un peso de contribución a cada conexión. Finalmente, las conexiones significativas serán en conjunto más pequeño que logre el mejor acierto de clasificación. La segunda metodología, se utiliza en un estudio de los patrones de conectividad significativos en redes de atención. Para esto, se extrae la conectividad de dos clases: target y no target en un experimento de paradigma Oddball. Aquí, las conexiones significativas se seleccionan como las que más se diferencian, estadísticamente, entre target y no target. Finalmente, en un estudio de recuperación de un sujeto con afasia, se encontraron diferencias en la conectividad relacionadas con las mejoras producidas terapia. Conexiones que cambian a través de las sesiones de terapia a nivel de amplitud y de estructura fueron extraídas. Además, se definieron y se seleccionaron como conexiones significativas las cuales tienen un cambio creciente entre las sesiones. Para todas las metodologías propuestas, la conectividad cerebral se calcula sobre señales de EEG y la extracción de las conexiones significativas se basa en información inherente a los datos o el experimento. En general, la selección de conexiones permite una reducción considerable de características de conectividad, esto facilita la interpretación fisiológica de los experimentos y puede mejorar el rendimiento y el costo computacional de los sistemas que utilizan estas característicasMaestríaapplication/pdfspaUniversidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y ComputaciónDepartamento de Ingeniería Eléctrica, Electrónica y ComputaciónHurtado Rincón, Juana Valeria (2018) Brain connectivity-patterns representation based on electroencephalography network analysis. Maestría thesis, Universidad Nacional de Colombia - Sede Manizales.62 Ingeniería y operaciones afines / EngineeringBrain connectivityElectroencephalographySignificant connectionssdffsdfConectividad cerebralElectroencefalografíaConexiones significativasBrain connectivity-patterns representation based on electroencephalography network analysisTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMORIGINAL1053823047.2018.pdfapplication/pdf7590186https://repositorio.unal.edu.co/bitstream/unal/64168/1/1053823047.2018.pdf0b2ee980982f452782b889bacb0fc3e0MD51THUMBNAIL1053823047.2018.pdf.jpg1053823047.2018.pdf.jpgGenerated Thumbnailimage/jpeg6006https://repositorio.unal.edu.co/bitstream/unal/64168/2/1053823047.2018.pdf.jpg999662ab59e05b9adb13d2b2599f4da8MD52unal/64168oai:repositorio.unal.edu.co:unal/641682023-04-25 23:09:12.623Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |