A new PLV-spatial filtering to improve the classification performance in BCI systems

—Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase...

ver descrição completa

Detalhes bibliográficos
Autores: Martín Chinea, Kevin, Gómez-González, José Franciso, Acosta Sánchez, Leopoldo
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/39045
Acesso em linha:http://riull.ull.es/xmlui/handle/915/39045
Access Level:acceso abierto
Palavra-chave:Electroencephalography
Phase locking value
Brain-computer interface
Machine learning
id ES_7e37cf7d47ba10cce5299bc6476bb128
oai_identifier_str oai:riull.ull.es:915/39045
network_acronym_str ES
network_name_str España
repository_id_str
spelling A new PLV-spatial filtering to improve the classification performance in BCI systemsMartín Chinea, KevinGómez-González, José FrancisoAcosta Sánchez, LeopoldoElectroencephalographyPhase locking valueBrain-computer interfaceMachine learning—Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.Ingeniería Industrial202420242022info:eu-repo/semantics/articleapplication/pdfhttp://riull.ull.es/xmlui/handle/915/39045reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, v. 30, 2275-2282Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ESoai:riull.ull.es:915/390452026-06-22T13:13:57Z
dc.title.none.fl_str_mv A new PLV-spatial filtering to improve the classification performance in BCI systems
title A new PLV-spatial filtering to improve the classification performance in BCI systems
spellingShingle A new PLV-spatial filtering to improve the classification performance in BCI systems
Martín Chinea, Kevin
Electroencephalography
Phase locking value
Brain-computer interface
Machine learning
title_short A new PLV-spatial filtering to improve the classification performance in BCI systems
title_full A new PLV-spatial filtering to improve the classification performance in BCI systems
title_fullStr A new PLV-spatial filtering to improve the classification performance in BCI systems
title_full_unstemmed A new PLV-spatial filtering to improve the classification performance in BCI systems
title_sort A new PLV-spatial filtering to improve the classification performance in BCI systems
dc.creator.none.fl_str_mv Martín Chinea, Kevin
Gómez-González, José Franciso
Acosta Sánchez, Leopoldo
author Martín Chinea, Kevin
author_facet Martín Chinea, Kevin
Gómez-González, José Franciso
Acosta Sánchez, Leopoldo
author_role author
author2 Gómez-González, José Franciso
Acosta Sánchez, Leopoldo
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería Industrial
dc.subject.none.fl_str_mv Electroencephalography
Phase locking value
Brain-computer interface
Machine learning
topic Electroencephalography
Phase locking value
Brain-computer interface
Machine learning
description —Objective: The performance of an EEG-based brain-computer interface (BCI) system is highly dependent on signal preprocessing. This manuscript presents a filtering method to improve the feature classification algorithms typically used in BCI. Methods: A graph Laplacian quadratic form using the Phase Locking Value (PLV) is applied to generate a new filtered signal in the preprocessing stage. Results: The accuracy of the classification algorithms improved significantly (up to 27.18% in the BCI Competition IV dataset, and up to 42.56% with records made with an Emotiv EPOC+). In addition, the proposed filtering algorithm has similar or better results when compared with the Filter Bank Common Spatial Pattern (FBCSP), which has disadvantages in a multiclass classification. Conclusion: This paper shows how our PLV-based filtering between EEG channels could improve the performance of a BCI.
publishDate 2022
dc.date.none.fl_str_mv 2022
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/39045
url http://riull.ull.es/xmlui/handle/915/39045
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, v. 30, 2275-2282
dc.rights.none.fl_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
rights_invalid_str_mv Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411720308457472
score 15,811543

Registos relacionados