Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFU |
Texto Completo: | https://repositorio.ufu.br/handle/123456789/37784 http://doi.org/10.14393/ufu.te.2023.8032 |
Resumo: | Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single channel approaches are scarce. In this context, this study proposed a single channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study. |
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Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyographyAbordagem de canal único para filtragem de sinais electroencefalográficos fortemente contaminados com electromiografia facialEMGEEGprocessingdecompositionelectromyographysignalfacialCNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOSEliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single channel approaches are scarce. In this context, this study proposed a single channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoTese (Doutorado)Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single channel approaches are scarce. In this context, this study proposed a single channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study.Universidade Federal de UberlândiaBrasilPrograma de Pós-graduação em Engenharia ElétricaAndrade, Adriano de Oliveirahttp://lattes.cnpq.br/1229329519982110Salinet Jr, João Loureshttp://lattes.cnpq.br/8831381008404112Abromavicius, Vytautashttps://orcid.org/0000-0003-1588-6572Carneiro, Pedro Cunhahttp://lattes.cnpq.br/6699870054095600Bernardes, Wellington Maycon Santoshttp://lattes.cnpq.br/8631549983581675Queiroz, Carlos Magno Medeiros2023-05-02T13:44:34Z2023-05-02T13:44:34Z2022-11-04info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfQUEIROZ,Carlos Magno Medeiros. Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography. 2022. 91 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2023. DOI http://doi.org/10.14393/ufu.te.2023.8032https://repositorio.ufu.br/handle/123456789/37784http://doi.org/10.14393/ufu.te.2023.8032enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFUinstname:Universidade Federal de Uberlândia (UFU)instacron:UFU2023-05-03T06:15:11Zoai:repositorio.ufu.br:123456789/37784Repositório InstitucionalONGhttp://repositorio.ufu.br/oai/requestdiinf@dirbi.ufu.bropendoar:2023-05-03T06:15:11Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography Abordagem de canal único para filtragem de sinais electroencefalográficos fortemente contaminados com electromiografia facial |
title |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
spellingShingle |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography Queiroz, Carlos Magno Medeiros EMG EEG processing decomposition electromyography signal facial CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
title_short |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
title_full |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
title_fullStr |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
title_full_unstemmed |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
title_sort |
Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography |
author |
Queiroz, Carlos Magno Medeiros |
author_facet |
Queiroz, Carlos Magno Medeiros |
author_role |
author |
dc.contributor.none.fl_str_mv |
Andrade, Adriano de Oliveira http://lattes.cnpq.br/1229329519982110 Salinet Jr, João Loures http://lattes.cnpq.br/8831381008404112 Abromavicius, Vytautas https://orcid.org/0000-0003-1588-6572 Carneiro, Pedro Cunha http://lattes.cnpq.br/6699870054095600 Bernardes, Wellington Maycon Santos http://lattes.cnpq.br/8631549983581675 |
dc.contributor.author.fl_str_mv |
Queiroz, Carlos Magno Medeiros |
dc.subject.por.fl_str_mv |
EMG EEG processing decomposition electromyography signal facial CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
topic |
EMG EEG processing decomposition electromyography signal facial CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA::BIOENGENHARIA::PROCESSAMENTO DE SINAIS BIOLOGICOS |
description |
Eliminating facial electromyographic (EMG) signal from the electroencephalogram (EEG) is crucial for the accuracy of applications such as brain computer interfaces (BCIs) and brain functionality measurement. Facial electromyography typically corrupts the electroencephalogram. Although it is possible to find in the literature a number of multi-channel approaches for filtering corrupted EEG, studies employing single channel approaches are scarce. In this context, this study proposed a single channel method for attenuating facial EMG noise from contaminated EEG. The architecture of the method allows for the evaluation and incorporation of multiple decomposition and adaptive filtering techniques. The decomposition method was responsible for generating EEG or EMG reference signals for the adaptive filtering stage. In this study, the decomposition techniques CiSSA, EMD, EEMD, EMD-PCA, SSA, and Wavelet were evaluated. The adaptive filtering methods RLS, Wiener, LMS, and NLMS were investigated. A time and frequency domain set of features were estimated from experimental signals to evaluate the performance of the single channel method. This set of characteristics permitted the characterization of the contamination of distinct facial muscles, namely Masseter, Frontalis, Zygomatic, Orbicularis Oris, and Orbicularis Oculi. Data were collected from ten healthy subjects executing an experimental protocol that introduced the necessary variability to evaluate the filtering performance. The largest level of contamination was produced by the Masseter muscle, as determined by statistical analysis of the set of features and visualization of topological maps. Regarding the decomposition method, the SSA method allowed for the generation of more suitable reference signals, whereas the RLS and NLMS methods were more suitable when the reference signal was derived from the EEG. In addition, the LMS and RLS methods were more appropriate when the reference signal was the EMG. This study has a number of practical implications, including the use of filtering techniques to reduce EEG contamination caused by the activation of facial muscles required by distinct types of studies. All the developed code, including examples, is available to facilitate a more accurate reproduction and improvement of the results of this study. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-11-04 2023-05-02T13:44:34Z 2023-05-02T13:44:34Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
QUEIROZ,Carlos Magno Medeiros. Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography. 2022. 91 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2023. DOI http://doi.org/10.14393/ufu.te.2023.8032 https://repositorio.ufu.br/handle/123456789/37784 http://doi.org/10.14393/ufu.te.2023.8032 |
identifier_str_mv |
QUEIROZ,Carlos Magno Medeiros. Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography. 2022. 91 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2023. DOI http://doi.org/10.14393/ufu.te.2023.8032 |
url |
https://repositorio.ufu.br/handle/123456789/37784 http://doi.org/10.14393/ufu.te.2023.8032 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Engenharia Elétrica |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFU instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
instname_str |
Universidade Federal de Uberlândia (UFU) |
instacron_str |
UFU |
institution |
UFU |
reponame_str |
Repositório Institucional da UFU |
collection |
Repositório Institucional da UFU |
repository.name.fl_str_mv |
Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU) |
repository.mail.fl_str_mv |
diinf@dirbi.ufu.br |
_version_ |
1805569686874619904 |