Classification of auditory selective attention using spatial coherence and modular attention index

Detalhes bibliográficos
Autor(a) principal: Souza, Ana Paula de
Data de Publicação: 2018
Outros Autores: Soares, Quenaz B., Felix, Leonardo B., Mendes, Eduardo M. A. M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: https://doi.org/10.1016/j.cmpb.2018.10.002
http://www.locus.ufv.br/handle/123456789/24083
Resumo: Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. The latter is a new method using modular attention index. The new classifier was developed to implement an auditory BCI where a volunteer makes binary choices using selective attention under the amplitude-modulated tones stimulation. Contralateral and modular classifiers were applied to the electroencephalogram (EEG) recorded from 144 subjects under the BCI protocol. The best set of parameters (carriers of the stimulus, channels and trials of signal) for this BCI was investigated taking into consideration the hit rate and the information transfer rate. The best result obtained using the modular classifier was a hit rate of 91.67% and information transfer rate of 6.74 bits/min using 0.5 kHz/4.0 kHz as stimuli and three windows (5.10 sec of EEG signal). These results were obtained with five electrodes (C3, P3, F8, P4, O2) using exhaustive search to identify regions with greater coherence.The modular classifier - using electroencephalogram channels from the central, frontal, occipital and parietal areas - improves the performance of auditory BCIs based on selective attention.
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spelling Souza, Ana Paula deSoares, Quenaz B.Felix, Leonardo B.Mendes, Eduardo M. A. M.2019-03-25T12:32:57Z2019-03-25T12:32:57Z2018-110169-2607https://doi.org/10.1016/j.cmpb.2018.10.002http://www.locus.ufv.br/handle/123456789/24083Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. The latter is a new method using modular attention index. The new classifier was developed to implement an auditory BCI where a volunteer makes binary choices using selective attention under the amplitude-modulated tones stimulation. Contralateral and modular classifiers were applied to the electroencephalogram (EEG) recorded from 144 subjects under the BCI protocol. The best set of parameters (carriers of the stimulus, channels and trials of signal) for this BCI was investigated taking into consideration the hit rate and the information transfer rate. The best result obtained using the modular classifier was a hit rate of 91.67% and information transfer rate of 6.74 bits/min using 0.5 kHz/4.0 kHz as stimuli and three windows (5.10 sec of EEG signal). These results were obtained with five electrodes (C3, P3, F8, P4, O2) using exhaustive search to identify regions with greater coherence.The modular classifier - using electroencephalogram channels from the central, frontal, occipital and parietal areas - improves the performance of auditory BCIs based on selective attention.engComputer Methods and Programs in BiomedicineVolume 166, Pages 107-113, November 2018Elsevier B. V.info:eu-repo/semantics/openAccessSelective attentionAuditory brain-computer interfaceElectroencephalogramSpatial coherenceClassification of auditory selective attention using spatial coherence and modular attention indexinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALartigo.pdfartigo.pdfTexto completoapplication/pdf1054635https://locus.ufv.br//bitstream/123456789/24083/1/artigo.pdfd305616d1c3408d8d0a0b9740059cc6dMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://locus.ufv.br//bitstream/123456789/24083/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/240832019-03-25 09:34:02.808oai:locus.ufv.br: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Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452019-03-25T12:34:02LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.en.fl_str_mv Classification of auditory selective attention using spatial coherence and modular attention index
title Classification of auditory selective attention using spatial coherence and modular attention index
spellingShingle Classification of auditory selective attention using spatial coherence and modular attention index
Souza, Ana Paula de
Selective attention
Auditory brain-computer interface
Electroencephalogram
Spatial coherence
title_short Classification of auditory selective attention using spatial coherence and modular attention index
title_full Classification of auditory selective attention using spatial coherence and modular attention index
title_fullStr Classification of auditory selective attention using spatial coherence and modular attention index
title_full_unstemmed Classification of auditory selective attention using spatial coherence and modular attention index
title_sort Classification of auditory selective attention using spatial coherence and modular attention index
author Souza, Ana Paula de
author_facet Souza, Ana Paula de
Soares, Quenaz B.
Felix, Leonardo B.
Mendes, Eduardo M. A. M.
author_role author
author2 Soares, Quenaz B.
Felix, Leonardo B.
Mendes, Eduardo M. A. M.
author2_role author
author
author
dc.contributor.author.fl_str_mv Souza, Ana Paula de
Soares, Quenaz B.
Felix, Leonardo B.
Mendes, Eduardo M. A. M.
dc.subject.pt-BR.fl_str_mv Selective attention
Auditory brain-computer interface
Electroencephalogram
Spatial coherence
topic Selective attention
Auditory brain-computer interface
Electroencephalogram
Spatial coherence
description Brain-Computer Interfaces (BCIs) based on auditory selective attention have been receiving much attention because i) they are useful for completely paralyzed users since they do not require muscular effort or gaze and ii) focusing attention is a natural human ability. Several techniques - such as recently developed Spatial Coherence (SC) - have been proposed in order to optimize the BCI procedure. Thus, this work aims at investigating and comparing two strategies based on spatial coherence detection: contralateral and modular classifiers. The latter is a new method using modular attention index. The new classifier was developed to implement an auditory BCI where a volunteer makes binary choices using selective attention under the amplitude-modulated tones stimulation. Contralateral and modular classifiers were applied to the electroencephalogram (EEG) recorded from 144 subjects under the BCI protocol. The best set of parameters (carriers of the stimulus, channels and trials of signal) for this BCI was investigated taking into consideration the hit rate and the information transfer rate. The best result obtained using the modular classifier was a hit rate of 91.67% and information transfer rate of 6.74 bits/min using 0.5 kHz/4.0 kHz as stimuli and three windows (5.10 sec of EEG signal). These results were obtained with five electrodes (C3, P3, F8, P4, O2) using exhaustive search to identify regions with greater coherence.The modular classifier - using electroencephalogram channels from the central, frontal, occipital and parietal areas - improves the performance of auditory BCIs based on selective attention.
publishDate 2018
dc.date.issued.fl_str_mv 2018-11
dc.date.accessioned.fl_str_mv 2019-03-25T12:32:57Z
dc.date.available.fl_str_mv 2019-03-25T12:32:57Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
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dc.identifier.uri.fl_str_mv https://doi.org/10.1016/j.cmpb.2018.10.002
http://www.locus.ufv.br/handle/123456789/24083
dc.identifier.issn.none.fl_str_mv 0169-2607
identifier_str_mv 0169-2607
url https://doi.org/10.1016/j.cmpb.2018.10.002
http://www.locus.ufv.br/handle/123456789/24083
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartofseries.pt-BR.fl_str_mv Volume 166, Pages 107-113, November 2018
dc.rights.driver.fl_str_mv Elsevier B. V.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Elsevier B. V.
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publisher.none.fl_str_mv Computer Methods and Programs in Biomedicine
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