EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off

Detalhes bibliográficos
Autor(a) principal: Broniera Junior, Paulo [UNESP]
Data de Publicação: 2021
Outros Autores: Campos, Daniel Prado, Lazzaretti, André Eugenio, Nohama, Percy, Carvalho, Aparecido Augusto [UNESP], Krueger, Eddy, Minhoto Teixeira, Marcelo Carvalho [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.bspc.2021.102662
http://hdl.handle.net/11449/206252
Resumo: People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work.
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spelling EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-offBrain–computer interfaceClosed-loop systemsElectroencephalographyFunctional electrical stimulationMechanomyographyMotor ImagerySpinal cord injuryPeople with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56Universidade Tecnológica Federal do Paraná (UTFPR), Marcílio Dias, 635Universidade Tecnológica Federal do Paraná (UTFPR), Avenida Sete de Setembro 3165Universidade Estadual de Londrina (UEL) – Departamento de Anatomia Laboratório de Engenharia Neural e de Reabilitação, Rodovia Celso Garcia Cid – Pr 445, Km 380Instituto Senai de Tecnologia da Informação e Comunicação (ISTIC) Laboratório de Sistemas Eletrônicos - Embarcados e de Potência IoT e Manufatura 4.0, Rua Belém 844Universidade Estadual Paulista Júlio Mesquita Filho (UNESP) Faculdade de Engenharia Campus Ilha Solteira, Av. Brasil Sul, 56Universidade Estadual Paulista (Unesp)Universidade Tecnológica Federal do Paraná (UTFPR)Universidade Estadual de Londrina (UEL)IoT e Manufatura 4.0Broniera Junior, Paulo [UNESP]Campos, Daniel PradoLazzaretti, André EugenioNohama, PercyCarvalho, Aparecido Augusto [UNESP]Krueger, EddyMinhoto Teixeira, Marcelo Carvalho [UNESP]2021-06-25T10:29:02Z2021-06-25T10:29:02Z2021-07-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.bspc.2021.102662Biomedical Signal Processing and Control, v. 68.1746-81081746-8094http://hdl.handle.net/11449/20625210.1016/j.bspc.2021.1026622-s2.0-85104927858Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBiomedical Signal Processing and Controlinfo:eu-repo/semantics/openAccess2021-10-23T01:58:07Zoai:repositorio.unesp.br:11449/206252Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:29:43.624324Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
title EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
spellingShingle EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
Broniera Junior, Paulo [UNESP]
Brain–computer interface
Closed-loop systems
Electroencephalography
Functional electrical stimulation
Mechanomyography
Motor Imagery
Spinal cord injury
title_short EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
title_full EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
title_fullStr EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
title_full_unstemmed EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
title_sort EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
author Broniera Junior, Paulo [UNESP]
author_facet Broniera Junior, Paulo [UNESP]
Campos, Daniel Prado
Lazzaretti, André Eugenio
Nohama, Percy
Carvalho, Aparecido Augusto [UNESP]
Krueger, Eddy
Minhoto Teixeira, Marcelo Carvalho [UNESP]
author_role author
author2 Campos, Daniel Prado
Lazzaretti, André Eugenio
Nohama, Percy
Carvalho, Aparecido Augusto [UNESP]
Krueger, Eddy
Minhoto Teixeira, Marcelo Carvalho [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Tecnológica Federal do Paraná (UTFPR)
Universidade Estadual de Londrina (UEL)
IoT e Manufatura 4.0
dc.contributor.author.fl_str_mv Broniera Junior, Paulo [UNESP]
Campos, Daniel Prado
Lazzaretti, André Eugenio
Nohama, Percy
Carvalho, Aparecido Augusto [UNESP]
Krueger, Eddy
Minhoto Teixeira, Marcelo Carvalho [UNESP]
dc.subject.por.fl_str_mv Brain–computer interface
Closed-loop systems
Electroencephalography
Functional electrical stimulation
Mechanomyography
Motor Imagery
Spinal cord injury
topic Brain–computer interface
Closed-loop systems
Electroencephalography
Functional electrical stimulation
Mechanomyography
Motor Imagery
Spinal cord injury
description People with spinal cord injury (SCI) may have their paralyzed muscles activated through functional electrical stimulation (FES). This neuromodulation technique has been used frequently to assist in controlling the movement of neuroprostheses. Electroencephalography (EEG) is able to trigger FES from the motor imagery captured through movements intentions. This research presents an isometric neuromuscular control system of the quadriceps muscle activated by EEG. Additionally, the detection of neuromuscular fatigue through the mechanomyography (MMG) technique is proposed, which is used to shut-off the system. A pilot study was performed on a chronic 42-year-old paraplegic (no voluntary contraction below the spinal cord injury level T8) volunteer. To do so, the training procedure for EEG signals was divided into the calibration and feedback phases. In the first one, four EEG channels and the Linear Discriminant Analysis (LDA) classifier were used to classify between motor imagery of the right leg and remain at rest. The maximum accuracy obtained during this stage was 77%. In the feedback phase, the volunteer was able to activate FES through brain–computer interface (BCI) in two tests (defined as Test 1 and Test 2) with the same procedure in different days. The closed-loop force control was tested with the setpoint of 2 kgf and 2.5 kgf and proved to be stable on both tests, successfully turning off the FES using the fatigue threshold from the MMG signal, being the main contribution of this work.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T10:29:02Z
2021-06-25T10:29:02Z
2021-07-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.bspc.2021.102662
Biomedical Signal Processing and Control, v. 68.
1746-8108
1746-8094
http://hdl.handle.net/11449/206252
10.1016/j.bspc.2021.102662
2-s2.0-85104927858
url http://dx.doi.org/10.1016/j.bspc.2021.102662
http://hdl.handle.net/11449/206252
identifier_str_mv Biomedical Signal Processing and Control, v. 68.
1746-8108
1746-8094
10.1016/j.bspc.2021.102662
2-s2.0-85104927858
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Biomedical Signal Processing and Control
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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