Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints
Magneto/Electroencephalography (M/EEG)-based neuroimaging is a widely used noninvasive technique for functional analysis of neuronal activity. One of the most prominent advantages of using M/EEG measures is the very low implementation cost and its height temporal resolution. However, the number of l...
- Autores:
-
Grisales Franco, Fily Mateos
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/58198
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/58198
http://bdigital.unal.edu.co/54853/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
M/EEG
Inverse problem
Brain mapping
Source connectivity
Problema inverso
Mapeo cerebral
Conectividad en fuentes
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Universidad Nacional de Colombia |
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|
dc.title.spa.fl_str_mv |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
title |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
spellingShingle |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints 62 Ingeniería y operaciones afines / Engineering M/EEG Inverse problem Brain mapping Source connectivity Problema inverso Mapeo cerebral Conectividad en fuentes |
title_short |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
title_full |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
title_fullStr |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
title_full_unstemmed |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
title_sort |
Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints |
dc.creator.fl_str_mv |
Grisales Franco, Fily Mateos |
dc.contributor.advisor.spa.fl_str_mv |
Castellanos Domínguez, César Germán (Thesis advisor) |
dc.contributor.author.spa.fl_str_mv |
Grisales Franco, Fily Mateos |
dc.subject.ddc.spa.fl_str_mv |
62 Ingeniería y operaciones afines / Engineering |
topic |
62 Ingeniería y operaciones afines / Engineering M/EEG Inverse problem Brain mapping Source connectivity Problema inverso Mapeo cerebral Conectividad en fuentes |
dc.subject.proposal.spa.fl_str_mv |
M/EEG Inverse problem Brain mapping Source connectivity Problema inverso Mapeo cerebral Conectividad en fuentes |
description |
Magneto/Electroencephalography (M/EEG)-based neuroimaging is a widely used noninvasive technique for functional analysis of neuronal activity. One of the most prominent advantages of using M/EEG measures is the very low implementation cost and its height temporal resolution. However, the number of locations measuring magnetic/electrical is relatively small (a couple of hundreds at best) while the discretized brain activity generators (sources) are several thousand. This fact corresponds an ill-posed mathematical problem commonly known as the M/EEG inverse problem. To solve such problems, additional information must be apriori assumed to obtain a unique and optimal solution. In the present work, a methodology to improve the accuracy and interpretability of the inverse problem solution is proposed, using physiologically motivated assumptions. Firstly, a method constraining the solution to a sparse representation in the space-time domain is introduce given a set of methodologies to syntonize the present parameters. Secondly, we propose a new source connectivity approach explicitly including spatiotemporal information of the neural activity extracted from M/EEG recordings. The proposed methods are compared with the state-of-art techniques in a simulated environment, and afterward, are validated using real-world data. In general, the contributed approaches are efficient and competitive compared to state-of-art brain mapping methods |
publishDate |
2016 |
dc.date.issued.spa.fl_str_mv |
2016 |
dc.date.accessioned.spa.fl_str_mv |
2019-07-02T13:48:47Z |
dc.date.available.spa.fl_str_mv |
2019-07-02T13:48:47Z |
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/58198 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/54853/ |
url |
https://repositorio.unal.edu.co/handle/unal/58198 http://bdigital.unal.edu.co/54853/ |
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 |
Grisales Franco, Fily Mateos (2016) Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints. 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 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
institution |
Universidad Nacional de Colombia |
bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/58198/1/1059700212.2016.pdf https://repositorio.unal.edu.co/bitstream/unal/58198/2/1059700212.2016.pdf.jpg |
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spelling |
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-1Grisales Franco, Fily Mateosb3f73e8b-379d-479e-84c0-460d3496cede3002019-07-02T13:48:47Z2019-07-02T13:48:47Z2016https://repositorio.unal.edu.co/handle/unal/58198http://bdigital.unal.edu.co/54853/Magneto/Electroencephalography (M/EEG)-based neuroimaging is a widely used noninvasive technique for functional analysis of neuronal activity. One of the most prominent advantages of using M/EEG measures is the very low implementation cost and its height temporal resolution. However, the number of locations measuring magnetic/electrical is relatively small (a couple of hundreds at best) while the discretized brain activity generators (sources) are several thousand. This fact corresponds an ill-posed mathematical problem commonly known as the M/EEG inverse problem. To solve such problems, additional information must be apriori assumed to obtain a unique and optimal solution. In the present work, a methodology to improve the accuracy and interpretability of the inverse problem solution is proposed, using physiologically motivated assumptions. Firstly, a method constraining the solution to a sparse representation in the space-time domain is introduce given a set of methodologies to syntonize the present parameters. Secondly, we propose a new source connectivity approach explicitly including spatiotemporal information of the neural activity extracted from M/EEG recordings. The proposed methods are compared with the state-of-art techniques in a simulated environment, and afterward, are validated using real-world data. In general, the contributed approaches are efficient and competitive compared to state-of-art brain mapping methodsResumen : El mapeo cerebral basado en señales de magneto/electroencefalografía (M/EEG), es una técnica muy usada para el análisis de la actividad neuronal en forma no invasiva. Una de las ventajas que provee la utilización de señales M/EEG es su bajo costo de implementación además de su sobresaliente resolución temporal. Sin embargo el número de posiciones magnéticas/eléctricas medidas son extremadamente bajas comparadas con la cantidad de puntos discretizados dentro del cerebro sobre los cuales se debe realizar la estimación de la actividad. Esto conlleva a un problema mal condicionado comúnmente conocido como el problema inverso de M/EEG. Para resolver este tipo de problemas, información apriori debe ser supuesta para así obtener una solución única y óptima. En el presente trabajo investigativo, se propone una metodología para mejorar la exactitud e interpretación a la solución del problema inverso teniendo en cuenta el contexto fisiológico del problema. En primer lugar se propone un algoritmo en el cual se representa la actividad cerebral a través de un conjunto de funciones espacio-temporales dando metodologías para sintonizar los parámetros presentes. En segundo lugar, proponemos un nuevo enfoque mediante conectividad en fuentes que explícitamente incluye información espacial y temporal de la actividad neuronal extraída del M/EEG. Los métodos propuestos son comparados con métodos del estado del arte usando señales simuladas, y finalmente son validados usando datos reales de M/EEG. En general, los métodos propuestos son eficientes y competitivos en comparación a los métodos de referenciaMaestríaapplication/pdfspaUniversidad Nacional de Colombia Sede Manizales Facultad de Ingeniería y Arquitectura Departamento de Ingeniería Eléctrica, Electrónica y ComputaciónDepartamento de Ingeniería Eléctrica, Electrónica y ComputaciónGrisales Franco, Fily Mateos (2016) Brain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraints. Maestría thesis, Universidad Nacional de Colombia - Sede Manizales.62 Ingeniería y operaciones afines / EngineeringM/EEGInverse problemBrain mappingSource connectivityProblema inversoMapeo cerebralConectividad en fuentesBrain activity reconstruction from non-stationary M/EEG data using spatiotemporal constraintsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMORIGINAL1059700212.2016.pdfapplication/pdf7208379https://repositorio.unal.edu.co/bitstream/unal/58198/1/1059700212.2016.pdfd08bf293be67cf764bdc5127ad450015MD51THUMBNAIL1059700212.2016.pdf.jpg1059700212.2016.pdf.jpgGenerated Thumbnailimage/jpeg5957https://repositorio.unal.edu.co/bitstream/unal/58198/2/1059700212.2016.pdf.jpg53bcc126fbf10f336ea9b1753f786b99MD52unal/58198oai:repositorio.unal.edu.co:unal/581982023-03-26 23:06:25.942Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |