Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/185765 |
Resumo: | This work proposes the classification of motor imagery signals for brain-machine interfaces with functional electrical stimulation in the quadriceps muscle. Five volunteers participated in the test, 3 healthy participants, aged 28 +/- 3 years, and 2 paraplegic volunteers, aged 43 (ASIA-B, C7 level - 16 years) and 47 (ASIA-A, T7 level - 20 years) years respectively. In total, each participant performed 90 repetitions of motor imaging of the lower limb under electrical stimulation, with frequencies of 20Hz, 35Hz, and 50Hz and current amplitude of 20mA. The patterns were analyzed off-line and submitted to the classification architectures after application of spatial filtering to extract the characteristics. The classification of the patterns was performed using the architectures: (i) Linear Discriminant Analysis (LDA), (ii) Multilayer Perceptron (MLP), and (iii) Support Vector Machine (SVM). To validate the proposal, the performance was compared between the classifiers through the accuracy of cross validation, variance, precision, and sensitivity. With the SVM classifier, the best accuracy percentage was 86.5%. These results are promising and the trained architectures are feasible for implementation in neuroprostheses with lower computational resources. |
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Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscleThis work proposes the classification of motor imagery signals for brain-machine interfaces with functional electrical stimulation in the quadriceps muscle. Five volunteers participated in the test, 3 healthy participants, aged 28 +/- 3 years, and 2 paraplegic volunteers, aged 43 (ASIA-B, C7 level - 16 years) and 47 (ASIA-A, T7 level - 20 years) years respectively. In total, each participant performed 90 repetitions of motor imaging of the lower limb under electrical stimulation, with frequencies of 20Hz, 35Hz, and 50Hz and current amplitude of 20mA. The patterns were analyzed off-line and submitted to the classification architectures after application of spatial filtering to extract the characteristics. The classification of the patterns was performed using the architectures: (i) Linear Discriminant Analysis (LDA), (ii) Multilayer Perceptron (MLP), and (iii) Support Vector Machine (SVM). To validate the proposal, the performance was compared between the classifiers through the accuracy of cross validation, variance, precision, and sensitivity. With the SVM classifier, the best accuracy percentage was 86.5%. These results are promising and the trained architectures are feasible for implementation in neuroprostheses with lower computational resources.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)UNESP Sao Paulo State Univ, Master & PhD Program Elect Engn, Campus Ilha Solteira, Sao Paulo, BrazilUTFPR Fed Univ Technol Parana, COELT Elect Engn, Campus Apucarana, Londrina, BrazilFed Univ Technol, Grad Program Elect & Comp Engn CPGEI, Curitiba, Parana, BrazilPontifical Catholic Univ Parana PUCPR, Rehabil Engn Lab PPGTS, Curitiba, Parana, BrazilUniv Estadual Londrina, Neural Engn & Rehabil Lab, Master & PhD Program Rehabil Sci, Londrina, BrazilUNESP Sao Paulo State Univ, Master & PhD Program Elect Engn, Campus Ilha Solteira, Sao Paulo, BrazilCNPq: 151210/2018-7CAPES: 001IeeeUniversidade Estadual Paulista (Unesp)UTFPR Fed Univ Technol ParanaFed Univ TechnolPontifical Catholic Univ Parana PUCPRUniversidade Estadual de Londrina (UEL)Broniera Junior, P. [UNESP]Nunes, W. R. B. M.Lazzaretti, A. E.Nohama, P.Carvalho, A. A. [UNESP]Krueger, E.Teixeira, M. C. M. [UNESP]IEEE2019-10-04T12:38:20Z2019-10-04T12:38:20Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject526-5292019 9th International Ieee/embs Conference On Neural Engineering (ner). New York: Ieee, p. 526-529, 2019.1948-3546http://hdl.handle.net/11449/185765WOS:000469933200129Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2019 9th International Ieee/embs Conference On Neural Engineering (ner)info:eu-repo/semantics/openAccess2024-07-04T19:11:44Zoai:repositorio.unesp.br:11449/185765Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:35:30.750678Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
title |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
spellingShingle |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle Broniera Junior, P. [UNESP] |
title_short |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
title_full |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
title_fullStr |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
title_full_unstemmed |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
title_sort |
Classifier for motor imagery during parametric functional electrical stimulation frequencies on the quadriceps muscle |
author |
Broniera Junior, P. [UNESP] |
author_facet |
Broniera Junior, P. [UNESP] Nunes, W. R. B. M. Lazzaretti, A. E. Nohama, P. Carvalho, A. A. [UNESP] Krueger, E. Teixeira, M. C. M. [UNESP] IEEE |
author_role |
author |
author2 |
Nunes, W. R. B. M. Lazzaretti, A. E. Nohama, P. Carvalho, A. A. [UNESP] Krueger, E. Teixeira, M. C. M. [UNESP] IEEE |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) UTFPR Fed Univ Technol Parana Fed Univ Technol Pontifical Catholic Univ Parana PUCPR Universidade Estadual de Londrina (UEL) |
dc.contributor.author.fl_str_mv |
Broniera Junior, P. [UNESP] Nunes, W. R. B. M. Lazzaretti, A. E. Nohama, P. Carvalho, A. A. [UNESP] Krueger, E. Teixeira, M. C. M. [UNESP] IEEE |
description |
This work proposes the classification of motor imagery signals for brain-machine interfaces with functional electrical stimulation in the quadriceps muscle. Five volunteers participated in the test, 3 healthy participants, aged 28 +/- 3 years, and 2 paraplegic volunteers, aged 43 (ASIA-B, C7 level - 16 years) and 47 (ASIA-A, T7 level - 20 years) years respectively. In total, each participant performed 90 repetitions of motor imaging of the lower limb under electrical stimulation, with frequencies of 20Hz, 35Hz, and 50Hz and current amplitude of 20mA. The patterns were analyzed off-line and submitted to the classification architectures after application of spatial filtering to extract the characteristics. The classification of the patterns was performed using the architectures: (i) Linear Discriminant Analysis (LDA), (ii) Multilayer Perceptron (MLP), and (iii) Support Vector Machine (SVM). To validate the proposal, the performance was compared between the classifiers through the accuracy of cross validation, variance, precision, and sensitivity. With the SVM classifier, the best accuracy percentage was 86.5%. These results are promising and the trained architectures are feasible for implementation in neuroprostheses with lower computational resources. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-10-04T12:38:20Z 2019-10-04T12:38:20Z 2019-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
2019 9th International Ieee/embs Conference On Neural Engineering (ner). New York: Ieee, p. 526-529, 2019. 1948-3546 http://hdl.handle.net/11449/185765 WOS:000469933200129 |
identifier_str_mv |
2019 9th International Ieee/embs Conference On Neural Engineering (ner). New York: Ieee, p. 526-529, 2019. 1948-3546 WOS:000469933200129 |
url |
http://hdl.handle.net/11449/185765 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2019 9th International Ieee/embs Conference On Neural Engineering (ner) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
526-529 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1808129092583686144 |