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http://www.ru.iimas.unam.mx/handle/IIMAS_UNAM/ART12
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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | Garduño, Edgar | - |
dc.contributor.author | Bribiesca, Ernesto | - |
dc.contributor.author | Yáñez-Suárez, Oscar | - |
dc.contributor.author | Medina-Bañuelos, Verónica | - |
dc.coverage.spatial | US | - |
dc.creator | Alvarado-González, Montserrat | - |
dc.date.accessioned | 2021-11-19T23:11:31Z | - |
dc.date.available | 2021-11-19T23:11:31Z | - |
dc.date.issued | 2016-01-10 | - |
dc.identifier.citation | Alvarado-González M.,Garduño E.,Bribiesca, E.,Yáñez-Suárez, O.,Medina-Bañuelos, V.(2016) P300 Detection Based on EEG Shape Features. Computational and Mathematical Methods in Medicine. doi:10.1155/2016/2029791 2016 2029791 | - |
dc.identifier.uri | http://www.ru.iimas.unam.mx/handle/IIMAS_UNAM/ART12 | - |
dc.description.abstract | We present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally, we present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s; we also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals. Our experiments with 21 subjects showed that the SWLDA’s performance using our shape-feature vector was , that is, higher than the one obtained with BCI2000-feature’s vector. The shape-feature vector is 34-dimensional for every electrode; however, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm showed an averaged area under the ROC (AUROC) curve of . Also, most of the subjects needed less than trials to have an AUROC superior to . Finally, we found that the electrode C4 also leads to better classification.. | - |
dc.format | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | Hindawi | - |
dc.rights | openAccess | - |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0 | - |
dc.source | Computational and Mathematical Methods in Medicine (1748-670X) ,Vol. 2016, Article ID 2029791, 14 pages, (2016). | - |
dc.subject | Signal Processing | - |
dc.subject | Image Processing | - |
dc.subject | Physiological Data | - |
dc.subject.classification | Ingeniería y Tecnología | - |
dc.title | P300 Detection Based on EEG Shape Features | - |
dc.type | article | - |
dc.type | publishedVersion | - |
dcterms.creator | Alvarado-González, Alicia Montserrat::orcid::0000-0002-3163-0537 | - |
dcterms.creator | GARDUÑO ANGELES, EDGAR::cvu::81894 | - |
dcterms.creator | BRIBIESCA CORREA, ERNESTO::cvu::13478 | - |
dcterms.creator | YAÑEZ SUAREZ, OSCAR::cvu::60662 | - |
dcterms.creator | MEDINA BAÑUELOS, VERONICA::cvu::13619 | - |
dc.audience | researchers | - |
dc.audience | students | - |
dc.audience | teachers | - |
dc.identifier.doi | http://dx.doi.org/10.1155/2016/2029791 | - |
dc.relation.ispartofjournal | https://www.hindawi.com/journals/cmmm/contents/year/2016/ | - |
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