dc.contributor.author | Rodríguez Moreno, Itsaso | |
dc.contributor.author | Irigoyen Garbizu, Itziar | |
dc.contributor.author | Sierra Araujo, Basilio | |
dc.contributor.author | Arenas Solá, Concepción | |
dc.date.accessioned | 2024-02-08T10:17:10Z | |
dc.date.available | 2024-02-08T10:17:10Z | |
dc.date.issued | 2022-12-20 | |
dc.identifier.citation | The R Journal 14(3) : 80-94 (2022) | |
dc.identifier.issn | 2073-4859 | |
dc.identifier.uri | http://hdl.handle.net/10810/65230 | |
dc.description.abstract | Common Spatial Patterns (CSP) is a widely used method to analyse electroencephalography
(EEG) data, concerning the supervised classification of the activity of brain. More generally, it can
be useful to distinguish between multivariate signals recorded during a time span for two different
classes. CSP is based on the simultaneous diagonalization of the average covariance matrices of signals
from both classes and it allows the data to be projected into a low-dimensional subspace. Once the
data are represented in a low-dimensional subspace, a classification step must be carried out. The
original CSP method is based on the Euclidean distance between signals, and here we extend it so that
it can be applied on any appropriate distance for data at hand. Both the classical CSP and the new
Distance-Based CSP (DB-CSP) are implemented in an R package, called dbcsp. | es_ES |
dc.description.sponsorship | This research was partially supported: IR by The Spanish Ministry of Science, Innovation and Universities (FPU18/04737 predoctoral grant). II by the Spanish Ministerio de Economia y Competitividad (RTI2018-093337-B-I00; PID2019-106942RB-C31). CA by the Spanish Ministerio de Economia y Competitividad (RTI2018-093337-B-I00, RTI2018-100968-B-I00) and by Grant 2017SGR622 (GRBIO) from the Departament d’Economia i Coneixement de la Generalitat de Catalunya. BS II by the Spanish Ministerio de Economia y Competitividad (RTI2018-093337-B-I00). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | The R Foundation | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICIU/FPU18/0473 | |
dc.relation | info:eu-repo/grantAgreement/MICIU/RTI2018-093337-B-I00 | |
dc.relation | info:eu-repo/grantAgreement/MICINN/PID2019-106942RB-C | |
dc.relation | info:eu-repo/grantAgreement/MICIU/RTI2018-100968-B-I00 | |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | R | es_ES |
dc.subject | CSP | es_ES |
dc.subject | distances | es_ES |
dc.title | dbcsp: User-friendly R package for Distance-Based Common Spatial Patterns | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...". | |
dc.relation.publisherversion | https://journal.r-project.org/articles/RJ-2022-044/ | es_ES |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |