EEG-FES-Force-MMG closed-loop control systems of a volunteer with paraplegia considering motor imagery with fatigue recognition and automatic shut-off
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , |
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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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 |
|
_version_ |
1808128660367998976 |