Approximate average head models for EEG source imaging

Autores
Valdés Hernández, Pedro A.; Von Ellenrieder, Nicolás; Ojeda Gonzalez, Alejandro; Kochen, Sara Silvia; Alemán Gómez, Yasser; Muravchik, Carlos Horacio; Valdés Sosa, Pedro A.
Año de publicación
2009
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual’s MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.
Fil: Valdés Hernández, Pedro A.. Centro Cubano de Neurociencias; Cuba
Fil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina
Fil: Ojeda Gonzalez, Alejandro. Centro Cubano de Neurociencias; Cuba
Fil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina
Fil: Alemán Gómez, Yasser. Centro Cubano de Neurociencias; Cuba
Fil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Valdés Sosa, Pedro A.. Centro Cubano de Neurociencias; Cuba
Materia
APPROXIMATE HEAD MODEL
AVERAGE
BEM
EEG CUBAN BRAIN MAPPING PROJECT
ELECTRODE MEASUREMENT
LEAD FIELD
MNI
SLORETA
THIN PLATE SPLINE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/115109
Acceder
id CONICETDig_e3662bb64422ec0879e25fc7824c7da1
oai_identifier_str oai:ri.conicet.gov.ar:11336/115109
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Approximate average head models for EEG source imagingValdés Hernández, Pedro A.Von Ellenrieder, NicolásOjeda Gonzalez, AlejandroKochen, Sara SilviaAlemán Gómez, YasserMuravchik, Carlos HoracioValdés Sosa, Pedro A.APPROXIMATE HEAD MODELAVERAGEBEMEEG CUBAN BRAIN MAPPING PROJECTELECTRODE MEASUREMENTLEAD FIELDMNISLORETATHIN PLATE SPLINEhttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual’s MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.Fil: Valdés Hernández, Pedro A.. Centro Cubano de Neurociencias; CubaFil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; ArgentinaFil: Ojeda Gonzalez, Alejandro. Centro Cubano de Neurociencias; CubaFil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Alemán Gómez, Yasser. Centro Cubano de Neurociencias; CubaFil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Valdés Sosa, Pedro A.. Centro Cubano de Neurociencias; CubaElsevier Science2009-12-15info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/115109Valdés Hernández, Pedro A.; Von Ellenrieder, Nicolás; Ojeda Gonzalez, Alejandro; Kochen, Sara Silvia; Alemán Gómez, Yasser; et al.; Approximate average head models for EEG source imaging; Elsevier Science; Journal of Neuroscience Methods; 185; 1; 15-12-2009; 125-1320165-0270CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S016502700900497X?via%3Dihubinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jneumeth.2009.09.005info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:04:43Zoai:ri.conicet.gov.ar:11336/115109instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:04:43.916CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Approximate average head models for EEG source imaging
title Approximate average head models for EEG source imaging
spellingShingle Approximate average head models for EEG source imaging
Valdés Hernández, Pedro A.
APPROXIMATE HEAD MODEL
AVERAGE
BEM
EEG CUBAN BRAIN MAPPING PROJECT
ELECTRODE MEASUREMENT
LEAD FIELD
MNI
SLORETA
THIN PLATE SPLINE
title_short Approximate average head models for EEG source imaging
title_full Approximate average head models for EEG source imaging
title_fullStr Approximate average head models for EEG source imaging
title_full_unstemmed Approximate average head models for EEG source imaging
title_sort Approximate average head models for EEG source imaging
dc.creator.none.fl_str_mv Valdés Hernández, Pedro A.
Von Ellenrieder, Nicolás
Ojeda Gonzalez, Alejandro
Kochen, Sara Silvia
Alemán Gómez, Yasser
Muravchik, Carlos Horacio
Valdés Sosa, Pedro A.
author Valdés Hernández, Pedro A.
author_facet Valdés Hernández, Pedro A.
Von Ellenrieder, Nicolás
Ojeda Gonzalez, Alejandro
Kochen, Sara Silvia
Alemán Gómez, Yasser
Muravchik, Carlos Horacio
Valdés Sosa, Pedro A.
author_role author
author2 Von Ellenrieder, Nicolás
Ojeda Gonzalez, Alejandro
Kochen, Sara Silvia
Alemán Gómez, Yasser
Muravchik, Carlos Horacio
Valdés Sosa, Pedro A.
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv APPROXIMATE HEAD MODEL
AVERAGE
BEM
EEG CUBAN BRAIN MAPPING PROJECT
ELECTRODE MEASUREMENT
LEAD FIELD
MNI
SLORETA
THIN PLATE SPLINE
topic APPROXIMATE HEAD MODEL
AVERAGE
BEM
EEG CUBAN BRAIN MAPPING PROJECT
ELECTRODE MEASUREMENT
LEAD FIELD
MNI
SLORETA
THIN PLATE SPLINE
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual’s MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.
Fil: Valdés Hernández, Pedro A.. Centro Cubano de Neurociencias; Cuba
Fil: Von Ellenrieder, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina
Fil: Ojeda Gonzalez, Alejandro. Centro Cubano de Neurociencias; Cuba
Fil: Kochen, Sara Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina
Fil: Alemán Gómez, Yasser. Centro Cubano de Neurociencias; Cuba
Fil: Muravchik, Carlos Horacio. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Valdés Sosa, Pedro A.. Centro Cubano de Neurociencias; Cuba
description We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual’s MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.
publishDate 2009
dc.date.none.fl_str_mv 2009-12-15
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/115109
Valdés Hernández, Pedro A.; Von Ellenrieder, Nicolás; Ojeda Gonzalez, Alejandro; Kochen, Sara Silvia; Alemán Gómez, Yasser; et al.; Approximate average head models for EEG source imaging; Elsevier Science; Journal of Neuroscience Methods; 185; 1; 15-12-2009; 125-132
0165-0270
CONICET Digital
CONICET
url http://hdl.handle.net/11336/115109
identifier_str_mv Valdés Hernández, Pedro A.; Von Ellenrieder, Nicolás; Ojeda Gonzalez, Alejandro; Kochen, Sara Silvia; Alemán Gómez, Yasser; et al.; Approximate average head models for EEG source imaging; Elsevier Science; Journal of Neuroscience Methods; 185; 1; 15-12-2009; 125-132
0165-0270
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S016502700900497X?via%3Dihub
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jneumeth.2009.09.005
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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