Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation

Quantitative electroencephalographic (EEG) analysis is very useful for diagnosing dysfunctional neural states and for evaluating drug effects on the brain, among others. However, the bidirectional contamination between electrooculographic (EOG) and cerebral activities can mislead and induce wrong co...

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Detalhes bibliográficos
Autores: Romero Lafuente, Sergio|||0000-0002-8627-543X, Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083, Barbanoj, Manel J.
Formato: artículo
Fecha de publicación:2009
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/9379
Acesso em linha:https://hdl.handle.net/2117/9379
https://dx.doi.org/10.1007/s10439-008-9589-6
Access Level:acceso abierto
Palavra-chave:Electroencephalography.
Electrooculography.
Electroencefalografia
Òptica aplicada
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Ciències de la visió
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oai_identifier_str oai:upcommons.upc.edu:2117/9379
network_acronym_str ES
network_name_str España
repository_id_str
spelling Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source SeparationRomero Lafuente, Sergio|||0000-0002-8627-543XMañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083Barbanoj, Manel J.Electroencephalography.Electrooculography.ElectroencefalografiaÒptica aplicadaÀrees temàtiques de la UPC::Ciències de la salutÀrees temàtiques de la UPC::Ciències de la visióQuantitative electroencephalographic (EEG) analysis is very useful for diagnosing dysfunctional neural states and for evaluating drug effects on the brain, among others. However, the bidirectional contamination between electrooculographic (EOG) and cerebral activities can mislead and induce wrong conclusions from EEG recordings. Different methods for ocular reduction have been developed but only few studies have shown an objective evaluation of their performance. For this purpose, the following approaches were evaluated with simulated data: regression analysis, adaptive filtering, and blind source separation (BSS). In the first two, filtered versions were also taken into account by filtering EOG references in order to reduce the cancellation of cerebral high frequency components in EEG data. Performance of these methods was quantitatively evaluated by level of similarity, agreement and errors in spectral variables both between sources and corrected EEG recordings. Topographic distributions showed that errors were located at anterior sites and especially in frontopolar and lateral–frontal regions. In addition, these errors were higher in theta and especially delta band. In general, filtered versions of time-domain regression and of adaptive filtering with RLS algorithm provided a very effective ocular reduction. However, BSS based on second order statistics showed the highest similarity indexes and the lowest errors in spectral variables.20092009-01-0120102010-10-05journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/9379https://dx.doi.org/10.1007/s10439-008-9589-6reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/93792026-05-27T15:37:01Z
dc.title.none.fl_str_mv Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
title Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
spellingShingle Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
Romero Lafuente, Sergio|||0000-0002-8627-543X
Electroencephalography.
Electrooculography.
Electroencefalografia
Òptica aplicada
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Ciències de la visió
title_short Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
title_full Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
title_fullStr Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
title_full_unstemmed Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
title_sort Ocular Reduction in EEG Signals Based on Adaptive Filtering, Regression and Blind Source Separation
dc.creator.none.fl_str_mv Romero Lafuente, Sergio|||0000-0002-8627-543X
Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Barbanoj, Manel J.
author Romero Lafuente, Sergio|||0000-0002-8627-543X
author_facet Romero Lafuente, Sergio|||0000-0002-8627-543X
Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Barbanoj, Manel J.
author_role author
author2 Mañanas Villanueva, Miguel Ángel|||0000-0001-9836-6083
Barbanoj, Manel J.
author2_role author
author
dc.subject.none.fl_str_mv Electroencephalography.
Electrooculography.
Electroencefalografia
Òptica aplicada
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Ciències de la visió
topic Electroencephalography.
Electrooculography.
Electroencefalografia
Òptica aplicada
Àrees temàtiques de la UPC::Ciències de la salut
Àrees temàtiques de la UPC::Ciències de la visió
description Quantitative electroencephalographic (EEG) analysis is very useful for diagnosing dysfunctional neural states and for evaluating drug effects on the brain, among others. However, the bidirectional contamination between electrooculographic (EOG) and cerebral activities can mislead and induce wrong conclusions from EEG recordings. Different methods for ocular reduction have been developed but only few studies have shown an objective evaluation of their performance. For this purpose, the following approaches were evaluated with simulated data: regression analysis, adaptive filtering, and blind source separation (BSS). In the first two, filtered versions were also taken into account by filtering EOG references in order to reduce the cancellation of cerebral high frequency components in EEG data. Performance of these methods was quantitatively evaluated by level of similarity, agreement and errors in spectral variables both between sources and corrected EEG recordings. Topographic distributions showed that errors were located at anterior sites and especially in frontopolar and lateral–frontal regions. In addition, these errors were higher in theta and especially delta band. In general, filtered versions of time-domain regression and of adaptive filtering with RLS algorithm provided a very effective ocular reduction. However, BSS based on second order statistics showed the highest similarity indexes and the lowest errors in spectral variables.
publishDate 2009
dc.date.none.fl_str_mv 2009
2009-01-01
2010
2010-10-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/9379
https://dx.doi.org/10.1007/s10439-008-9589-6
url https://hdl.handle.net/2117/9379
https://dx.doi.org/10.1007/s10439-008-9589-6
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869405426877988864
score 15,301603

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