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Título : P300 Detection Based on EEG Shape Features
Autor: Alvarado-González, Montserrat
Otros autores : Garduño, Edgar
Bribiesca, Ernesto
Yáñez-Suárez, Oscar
Medina-Bañuelos, Verónica
En: Computational and Mathematical Methods in Medicine (1748-670X) ,Vol. 2016, Article ID 2029791, 14 pages, (2016).
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Editorial : Hindawi
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..
Area del conocimiento : Ingeniería y Tecnología
Palabras clave en inglés : Signal Processing
Image Processing
Physiological Data
Fecha de publicación : 10-ene-2016
Idioma: Inglés
Lugar: Estados Unidos
Citación : 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
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