A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition

Autores
Dai, Yangyang; Duan, Feng; Feng, Fan; Sun, Zhe; Zhang, Yu; Caiafa, César Federico; Marti Puig, Pere; Solé Casals, Jordi
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
Fil: Dai, Yangyang. Nankai University; China
Fil: Duan, Feng. Nankai University; China
Fil: Feng, Fan. Nankai University; China
Fil: Sun, Zhe. RIKEN; Japón
Fil: Zhang, Yu. Lehigh University Bethlehem; Estados Unidos
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Marti Puig, Pere. Central University of Catalonia; España
Fil: Solé Casals, Jordi. Central University of Catalonia; España
Materia
EEG
EMG artifact
signal serialization
EEMD
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:11336/146097
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/146097
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode DecompositionDai, YangyangDuan, FengFeng, FanSun, ZheZhang, YuCaiafa, César FedericoMarti Puig, PereSolé Casals, JordiEEGEMG artifactsignal serializationEEMDhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.Fil: Dai, Yangyang. Nankai University; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Sun, Zhe. RIKEN; JapónFil: Zhang, Yu. Lehigh University Bethlehem; Estados UnidosFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Marti Puig, Pere. Central University of Catalonia; EspañaFil: Solé Casals, Jordi. Central University of Catalonia; EspañaMolecular Diversity Preservation International2021-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/146097Dai, Yangyang; Duan, Feng; Feng, Fan; Sun, Zhe; Zhang, Yu; et al.; A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition; Molecular Diversity Preservation International; Entropy; 23; 1170; 9-2021; 1-161099-4300CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/23/9/1170/htminfo:eu-repo/semantics/altIdentifier/url/https://doi.org/10.3390/e23091170info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:00:04Zoai:ri.conicet.gov.ar:11336/146097instacron: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:34982025-09-29 10:00:04.337CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
title A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
spellingShingle A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
Dai, Yangyang
EEG
EMG artifact
signal serialization
EEMD
title_short A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
title_full A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
title_fullStr A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
title_full_unstemmed A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
title_sort A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition
dc.creator.none.fl_str_mv Dai, Yangyang
Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, César Federico
Marti Puig, Pere
Solé Casals, Jordi
author Dai, Yangyang
author_facet Dai, Yangyang
Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, César Federico
Marti Puig, Pere
Solé Casals, Jordi
author_role author
author2 Duan, Feng
Feng, Fan
Sun, Zhe
Zhang, Yu
Caiafa, César Federico
Marti Puig, Pere
Solé Casals, Jordi
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv EEG
EMG artifact
signal serialization
EEMD
topic EEG
EMG artifact
signal serialization
EEMD
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
Fil: Dai, Yangyang. Nankai University; China
Fil: Duan, Feng. Nankai University; China
Fil: Feng, Fan. Nankai University; China
Fil: Sun, Zhe. RIKEN; Japón
Fil: Zhang, Yu. Lehigh University Bethlehem; Estados Unidos
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Marti Puig, Pere. Central University of Catalonia; España
Fil: Solé Casals, Jordi. Central University of Catalonia; España
description An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD limits the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems.
publishDate 2021
dc.date.none.fl_str_mv 2021-09
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/11336/146097
Dai, Yangyang; Duan, Feng; Feng, Fan; Sun, Zhe; Zhang, Yu; et al.; A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition; Molecular Diversity Preservation International; Entropy; 23; 1170; 9-2021; 1-16
1099-4300
CONICET Digital
CONICET
url http://hdl.handle.net/11336/146097
identifier_str_mv Dai, Yangyang; Duan, Feng; Feng, Fan; Sun, Zhe; Zhang, Yu; et al.; A fast approach to removing muscle artifacts for EEG with signal serialization based Ensemble Empirical Mode Decomposition; Molecular Diversity Preservation International; Entropy; 23; 1170; 9-2021; 1-16
1099-4300
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/1099-4300/23/9/1170/htm
info:eu-repo/semantics/altIdentifier/url/https://doi.org/10.3390/e23091170
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
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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 13.070432

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