Unsupervised Common Spatial Patterns

The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present...

Descripción completa

Detalles Bibliográficos
Autores: Martín Clemente, Rubén, Olías Sánchez, Francisco Javier, Cruces Álvarez, Sergio Antonio, Antonio Zarzoso, Vicente
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/131369
Acceso en línea:https://hdl.handle.net/11441/131369
https://doi.org/10.1109/TNSRE.2019.2936411
Access Level:acceso abierto
Palabra clave:Common spatial patterns
Brain computer interfaces
Kurtosis
id ES_cca107ca015dfd26e5508622a961cfc8
oai_identifier_str oai:idus.us.es:11441/131369
network_acronym_str ES
network_name_str España
repository_id_str
spelling Unsupervised Common Spatial PatternsMartín Clemente, RubénOlías Sánchez, Francisco JavierCruces Álvarez, Sergio AntonioAntonio Zarzoso, VicenteCommon spatial patternsBrain computer interfacesKurtosisThe common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.Ministerio de Economía y Competitividad (España) TEC2017-82807-PInstitute of Electrical and Electronics Engineers Inc.Teoría de la Señal y ComunicacionesEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Laboratorio I3S2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/131369https://doi.org/10.1109/TNSRE.2019.2936411reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 27 (10), 2135-2144.TEC2017-82807-Phttps://ieeexplore.ieee.org/document/8844811info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1313692026-06-17T12:51:07Z
dc.title.none.fl_str_mv Unsupervised Common Spatial Patterns
title Unsupervised Common Spatial Patterns
spellingShingle Unsupervised Common Spatial Patterns
Martín Clemente, Rubén
Common spatial patterns
Brain computer interfaces
Kurtosis
title_short Unsupervised Common Spatial Patterns
title_full Unsupervised Common Spatial Patterns
title_fullStr Unsupervised Common Spatial Patterns
title_full_unstemmed Unsupervised Common Spatial Patterns
title_sort Unsupervised Common Spatial Patterns
dc.creator.none.fl_str_mv Martín Clemente, Rubén
Olías Sánchez, Francisco Javier
Cruces Álvarez, Sergio Antonio
Antonio Zarzoso, Vicente
author Martín Clemente, Rubén
author_facet Martín Clemente, Rubén
Olías Sánchez, Francisco Javier
Cruces Álvarez, Sergio Antonio
Antonio Zarzoso, Vicente
author_role author
author2 Olías Sánchez, Francisco Javier
Cruces Álvarez, Sergio Antonio
Antonio Zarzoso, Vicente
author2_role author
author
author
dc.contributor.none.fl_str_mv Teoría de la Señal y Comunicaciones
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Laboratorio I3S
dc.subject.none.fl_str_mv Common spatial patterns
Brain computer interfaces
Kurtosis
topic Common spatial patterns
Brain computer interfaces
Kurtosis
description The common spatial pattern (CSP) method is a dimensionality reduction technique widely used in brain-computer interface (BCI) systems. In the two-class CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner, i.e., with no need for labeled data, when the classes follow Gaussian or elliptically symmetric distributions. Numerical analyses on synthetic and real data validate these findings in various experimental conditions, and demonstrate the interest of the proposed unsupervised approach.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/131369
https://doi.org/10.1109/TNSRE.2019.2936411
url https://hdl.handle.net/11441/131369
https://doi.org/10.1109/TNSRE.2019.2936411
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, 27 (10), 2135-2144.
TEC2017-82807-P
https://ieeexplore.ieee.org/document/8844811
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869419737215139840
score 15,301603

Similares en LA Referencia