Classification of mental tasks using different spectral estimation methods

Autores
Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Avila, Enrique; Torres, Abel
Año de publicación
2009
Idioma
inglés
Tipo de recurso
parte de libro
Estado
versión publicada
Descripción
In this chapter, parametric (Burg) and non parametric (standard and Welch) spectral methods were utilized in order to estimate the spectral content of EEG signals for different mental tasks. Two parameters were utilized to analyze the behaviour of every spectral estimation methods: the Pm and the RMS of different frequency bands. These methods were tested in two different databases. We found that the use of the RMS allows higher classification accuracies with any spectral estimation technique. The Welch periodogram and Burg method are preferable in front of the standard periodogram. The use of Welch or Burg methods seems to be indistinct due to they performed similar, although in some subjects performed better one than other.
Fil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Avila, Enrique. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Torres, Abel. Universidad Politécnica de Catalunya; España
Materia
EEG SIGNAL PROCESSING
BRAIN COMPUTER INTERFACE
MENTAL TASKS IDENTIFICATION
SPECTRAL ESTIMATION METHODS
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:11336/159372
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Classification of mental tasks using different spectral estimation methodsDiez, Pablo FedericoLaciar Leber, EricMut, Vicente AntonioAvila, EnriqueTorres, AbelEEG SIGNAL PROCESSINGBRAIN COMPUTER INTERFACEMENTAL TASKS IDENTIFICATIONSPECTRAL ESTIMATION METHODShttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2In this chapter, parametric (Burg) and non parametric (standard and Welch) spectral methods were utilized in order to estimate the spectral content of EEG signals for different mental tasks. Two parameters were utilized to analyze the behaviour of every spectral estimation methods: the Pm and the RMS of different frequency bands. These methods were tested in two different databases. We found that the use of the RMS allows higher classification accuracies with any spectral estimation technique. The Welch periodogram and Burg method are preferable in front of the standard periodogram. The use of Welch or Burg methods seems to be indistinct due to they performed similar, although in some subjects performed better one than other.Fil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Avila, Enrique. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; ArgentinaFil: Torres, Abel. Universidad Politécnica de Catalunya; EspañaIntechOpenBarros de Mello, Carlos Alexandre2009info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookParthttp://purl.org/coar/resource_type/c_3248info:ar-repo/semantics/parteDeLibroapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/159372Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Avila, Enrique; Torres, Abel; Classification of mental tasks using different spectral estimation methods; IntechOpen; 2009; 287-306978-953-307-013-1CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.intechopen.com/chapters/8805info:eu-repo/semantics/altIdentifier/doi/10.5772/7863info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:43:01Zoai:ri.conicet.gov.ar:11336/159372instacron: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:34982025-09-29 10:43:02.192CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Classification of mental tasks using different spectral estimation methods
title Classification of mental tasks using different spectral estimation methods
spellingShingle Classification of mental tasks using different spectral estimation methods
Diez, Pablo Federico
EEG SIGNAL PROCESSING
BRAIN COMPUTER INTERFACE
MENTAL TASKS IDENTIFICATION
SPECTRAL ESTIMATION METHODS
title_short Classification of mental tasks using different spectral estimation methods
title_full Classification of mental tasks using different spectral estimation methods
title_fullStr Classification of mental tasks using different spectral estimation methods
title_full_unstemmed Classification of mental tasks using different spectral estimation methods
title_sort Classification of mental tasks using different spectral estimation methods
dc.creator.none.fl_str_mv Diez, Pablo Federico
Laciar Leber, Eric
Mut, Vicente Antonio
Avila, Enrique
Torres, Abel
author Diez, Pablo Federico
author_facet Diez, Pablo Federico
Laciar Leber, Eric
Mut, Vicente Antonio
Avila, Enrique
Torres, Abel
author_role author
author2 Laciar Leber, Eric
Mut, Vicente Antonio
Avila, Enrique
Torres, Abel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Barros de Mello, Carlos Alexandre
dc.subject.none.fl_str_mv EEG SIGNAL PROCESSING
BRAIN COMPUTER INTERFACE
MENTAL TASKS IDENTIFICATION
SPECTRAL ESTIMATION METHODS
topic EEG SIGNAL PROCESSING
BRAIN COMPUTER INTERFACE
MENTAL TASKS IDENTIFICATION
SPECTRAL ESTIMATION METHODS
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this chapter, parametric (Burg) and non parametric (standard and Welch) spectral methods were utilized in order to estimate the spectral content of EEG signals for different mental tasks. Two parameters were utilized to analyze the behaviour of every spectral estimation methods: the Pm and the RMS of different frequency bands. These methods were tested in two different databases. We found that the use of the RMS allows higher classification accuracies with any spectral estimation technique. The Welch periodogram and Burg method are preferable in front of the standard periodogram. The use of Welch or Burg methods seems to be indistinct due to they performed similar, although in some subjects performed better one than other.
Fil: Diez, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Mut, Vicente Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Avila, Enrique. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina
Fil: Torres, Abel. Universidad Politécnica de Catalunya; España
description In this chapter, parametric (Burg) and non parametric (standard and Welch) spectral methods were utilized in order to estimate the spectral content of EEG signals for different mental tasks. Two parameters were utilized to analyze the behaviour of every spectral estimation methods: the Pm and the RMS of different frequency bands. These methods were tested in two different databases. We found that the use of the RMS allows higher classification accuracies with any spectral estimation technique. The Welch periodogram and Burg method are preferable in front of the standard periodogram. The use of Welch or Burg methods seems to be indistinct due to they performed similar, although in some subjects performed better one than other.
publishDate 2009
dc.date.none.fl_str_mv 2009
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/bookPart
http://purl.org/coar/resource_type/c_3248
info:ar-repo/semantics/parteDeLibro
status_str publishedVersion
format bookPart
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/159372
Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Avila, Enrique; Torres, Abel; Classification of mental tasks using different spectral estimation methods; IntechOpen; 2009; 287-306
978-953-307-013-1
CONICET Digital
CONICET
url http://hdl.handle.net/11336/159372
identifier_str_mv Diez, Pablo Federico; Laciar Leber, Eric; Mut, Vicente Antonio; Avila, Enrique; Torres, Abel; Classification of mental tasks using different spectral estimation methods; IntechOpen; 2009; 287-306
978-953-307-013-1
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.intechopen.com/chapters/8805
info:eu-repo/semantics/altIdentifier/doi/10.5772/7863
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
dc.publisher.none.fl_str_mv IntechOpen
publisher.none.fl_str_mv IntechOpen
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 13.070432

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