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://dx.doi.org/10.1109/NER.2019.8717105 http://hdl.handle.net/11449/221304 |
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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Repositório Institucional da UNESP |
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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.São Paulo State University Campus Ilha SolteiraUTFPR - Federal University of Technology - Paraná Campus Apucarana COELT-Electrical EngineeringFederal University of TechnologyRehabilitation Engineering Laboratory/PPGTS Pontifical Catholic University of Paraná (PUCPR)State University of LondrinaSão Paulo State University Campus Ilha SolteiraUniversidade Estadual Paulista (UNESP)COELT-Electrical EngineeringFederal University of TechnologyPontifical Catholic University of Paraná (PUCPR)Universidade Estadual de Londrina (UEL)Broniera, P. [UNESP]Nunes, W. R.B.M.Lazzaretti, A. E.Nohama, P.Carvalho, A. A. [UNESP]Krueger, E.Teixeira, M. C.M. [UNESP]2022-04-28T19:27:22Z2022-04-28T19:27:22Z2019-05-16info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject526-529http://dx.doi.org/10.1109/NER.2019.8717105International IEEE/EMBS Conference on Neural Engineering, NER, v. 2019-March, p. 526-529.1948-35541948-3546http://hdl.handle.net/11449/22130410.1109/NER.2019.87171052-s2.0-85066744265Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational IEEE/EMBS Conference on Neural Engineering, NERinfo:eu-repo/semantics/openAccess2022-04-28T19:27:22Zoai:repositorio.unesp.br:11449/221304Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T22:38:49.635144Repositório 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, 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, P. [UNESP] |
author_facet |
Broniera, P. [UNESP] Nunes, W. R.B.M. Lazzaretti, A. E. Nohama, P. Carvalho, A. A. [UNESP] Krueger, E. Teixeira, M. C.M. [UNESP] |
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] |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) COELT-Electrical Engineering Federal University of Technology Pontifical Catholic University of Paraná (PUCPR) Universidade Estadual de Londrina (UEL) |
dc.contributor.author.fl_str_mv |
Broniera, P. [UNESP] Nunes, W. R.B.M. Lazzaretti, A. E. Nohama, P. Carvalho, A. A. [UNESP] Krueger, E. Teixeira, M. C.M. [UNESP] |
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 |
2019-05-16 2022-04-28T19:27:22Z 2022-04-28T19:27:22Z |
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 |
http://dx.doi.org/10.1109/NER.2019.8717105 International IEEE/EMBS Conference on Neural Engineering, NER, v. 2019-March, p. 526-529. 1948-3554 1948-3546 http://hdl.handle.net/11449/221304 10.1109/NER.2019.8717105 2-s2.0-85066744265 |
url |
http://dx.doi.org/10.1109/NER.2019.8717105 http://hdl.handle.net/11449/221304 |
identifier_str_mv |
International IEEE/EMBS Conference on Neural Engineering, NER, v. 2019-March, p. 526-529. 1948-3554 1948-3546 10.1109/NER.2019.8717105 2-s2.0-85066744265 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
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.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_ |
1808129446658441216 |