Bispectrum-based features classification for myoelectric control

Autores
Orosco, Eugenio Conrado; López Celani, Natalia Martina; Di Sciascio, Fernando Agustín
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.
Fil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Emg
Robust Bispectrum
Continuous Classification
Myoelectric Control
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/23954
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Bispectrum-based features classification for myoelectric controlOrosco, Eugenio ConradoLópez Celani, Natalia MartinaDi Sciascio, Fernando AgustínEmgRobust BispectrumContinuous ClassificationMyoelectric Controlhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.Fil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaElsevier2013-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/23954Orosco, Eugenio Conrado; López Celani, Natalia Martina; Di Sciascio, Fernando Agustín; Bispectrum-based features classification for myoelectric control; Elsevier; Biomedical Signal Processing and Control; 8; 3; 3-2013; 153-1681746-8094CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1746809412000900info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bspc.2012.08.008info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:11:07Zoai:ri.conicet.gov.ar:11336/23954instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:11:08.166CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Bispectrum-based features classification for myoelectric control
title Bispectrum-based features classification for myoelectric control
spellingShingle Bispectrum-based features classification for myoelectric control
Orosco, Eugenio Conrado
Emg
Robust Bispectrum
Continuous Classification
Myoelectric Control
title_short Bispectrum-based features classification for myoelectric control
title_full Bispectrum-based features classification for myoelectric control
title_fullStr Bispectrum-based features classification for myoelectric control
title_full_unstemmed Bispectrum-based features classification for myoelectric control
title_sort Bispectrum-based features classification for myoelectric control
dc.creator.none.fl_str_mv Orosco, Eugenio Conrado
López Celani, Natalia Martina
Di Sciascio, Fernando Agustín
author Orosco, Eugenio Conrado
author_facet Orosco, Eugenio Conrado
López Celani, Natalia Martina
Di Sciascio, Fernando Agustín
author_role author
author2 López Celani, Natalia Martina
Di Sciascio, Fernando Agustín
author2_role author
author
dc.subject.none.fl_str_mv Emg
Robust Bispectrum
Continuous Classification
Myoelectric Control
topic Emg
Robust Bispectrum
Continuous Classification
Myoelectric Control
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.
Fil: Orosco, Eugenio Conrado. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: López Celani, Natalia Martina. Universidad Nacional de San Juan. Facultad de Ingeniería. Departamento de Electrónica y Automática. Gabinete de Tecnología Médica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Di Sciascio, Fernando Agustín. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Surface electromyographic signals provide useful information about motion intentionality. Therefore, they are a suitable reference signal for control purposes. A continuous classification scheme of five upper limb movements applied to a myoelectric control of a robotic arm is presented. This classification is based on features extracted from the bispectrum of four EMG signal channels. Among several bispectrum estimators, this paper is focused on arithmetic mean, median, and trimmed mean estimators, and their ensemble average versions. All bispectrum estimators have been evaluated in terms of accuracy, robustness against outliers, and computational time. The median bispectrum estimator shows low variance and high robustness properties. Two feature reduction methods for the complex bispectrum matrix are proposed. The first one estimates the three classic means (arithmetic, harmonic, and geometric means) from the module of the bispectrum matrix, and the second one estimates the same three means from the square of the real part of the bispectrum matrix. A two-layer feedforward network for movement's classification and a dedicated system to achieve the myoelectric control of a robotic arm were used. It was found that the classification performance in real-time is similar to those obtained off-line by other authors, and that all volunteers in the practical application successfully completed the control task.
publishDate 2013
dc.date.none.fl_str_mv 2013-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/23954
Orosco, Eugenio Conrado; López Celani, Natalia Martina; Di Sciascio, Fernando Agustín; Bispectrum-based features classification for myoelectric control; Elsevier; Biomedical Signal Processing and Control; 8; 3; 3-2013; 153-168
1746-8094
CONICET Digital
CONICET
url http://hdl.handle.net/11336/23954
identifier_str_mv Orosco, Eugenio Conrado; López Celani, Natalia Martina; Di Sciascio, Fernando Agustín; Bispectrum-based features classification for myoelectric control; Elsevier; Biomedical Signal Processing and Control; 8; 3; 3-2013; 153-168
1746-8094
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1746809412000900
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.bspc.2012.08.008
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.993085

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