Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.creator | Peterson, Victoria | - |
| dc.creator | Rufiner, Hugo Leonardo | - |
| dc.creator | Spies, Ruben Daniel | - |
| dc.date | 2018-06-01T20:12:08Z | - |
| dc.date | 2018-06-01T20:12:08Z | - |
| dc.date | 2017-03 | - |
| dc.date | 2018-05-31T18:17:33Z | - |
| dc.date.accessioned | 2019-04-29T15:35:31Z | - |
| dc.date.available | 2019-04-29T15:35:31Z | - |
| dc.date.issued | 2018-06-01T20:12:08Z | - |
| dc.date.issued | 2018-06-01T20:12:08Z | - |
| dc.date.issued | 2017-03 | - |
| dc.date.issued | 2018-05-31T18:17:33Z | - |
| dc.identifier | Peterson, Victoria; Rufiner, Hugo Leonardo; Spies, Ruben Daniel; Generalized Sparse Discriminant Analysis for Event-Related Potential Classification; Elsevier; Biomedical Signal Processing and Control; 35; 3-2017 | - |
| dc.identifier | 1746-8094 | - |
| dc.identifier | http://hdl.handle.net/11336/47045 | - |
| dc.identifier | CONICET Digital | - |
| dc.identifier | CONICET | - |
| dc.identifier.uri | http://rodna.bn.gov.ar:8080/jspui/handle/bnmm/297339 | - |
| dc.description | A brain computer interface (BCI) is a system which provides direct communication between the mind of a person and the outside world by using only brain activity (EEG). The event-related potential (ERP)-based BCI problem consists of a binary pattern recognition. Linear discriminant analysis (LDA) is widely used to solve this type of classification problems, but it fails when the number of features is large relative to the number of observations. In this work we propose a penalized version of the sparse discriminant analysis (SDA), called generalized sparse discriminant analysis (GSDA), for binary classification. This method inherits both the discriminative feature selection and classification properties of SDA and it also improves SDA performance through the addition of Kullback-Leibler class discrepancy information. The GSDA method is designed to automatically select the optimal regularization parameters. Numerical experiments with two real ERP-EEG datasets show that, on one hand, GSDA outperforms standard SDA in the sense of classification performance, sparsity and required computing time, and, on the other hand, it also yields better overall performances, compared to well-known ERP classification algorithms, for single-trial ERP classification when insufficient training samples are available. Hence, GSDA constitute a potential useful method for reducing the calibration times in ERP-based BCI systems. | - |
| dc.description | Fil: Peterson, Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina | - |
| dc.description | Fil: Rufiner, Hugo Leonardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina. Universidad Nacional de Entre Ríos. Facultad de Ingeniería; Argentina | - |
| dc.description | Fil: Spies, Ruben Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Nacional del Litoral. Facultad de Ingeniería Química; Argentina | - |
| dc.format | application/pdf | - |
| dc.format | application/pdf | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.bspc.2017.03.001 | - |
| dc.rights | info:eu-repo/semantics/openAccess | - |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | - |
| dc.source | reponame:CONICET Digital (CONICET) | - |
| dc.source | instname:Consejo Nacional de Investigaciones Científicas y Técnicas | - |
| dc.source | instacron:CONICET | - |
| dc.subject | BRAIN-COMPUTER INTERFACE | - |
| dc.subject | EVENT-RELATED POTENTIAL | - |
| dc.subject | KULLBACJ-LEIBLER DIVERGENCE | - |
| dc.subject | PENALIZATION | - |
| dc.subject | SPARSE DISCRIMINANT ANALYSIS | - |
| dc.subject | Ciencias de la Computación | - |
| dc.subject | Ciencias de la Computación e Información | - |
| dc.subject | CIENCIAS NATURALES Y EXACTAS | - |
| dc.subject | Matemática Pura | - |
| dc.subject | Matemáticas | - |
| dc.subject | CIENCIAS NATURALES Y EXACTAS | - |
| dc.title | Generalized Sparse Discriminant Analysis for Event-Related Potential Classification | - |
| dc.type | info:eu-repo/semantics/article | - |
| dc.type | info:eu-repo/semantics/publishedVersion | - |
| dc.type | info:ar-repo/semantics/articulo | - |
| Aparece en las colecciones: | CONICET | |
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