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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 2029791, 14 pages
Research Article

P300 Detection Based on EEG Shape Features

1Graduate Program in Computer Science and Engineering, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico
2Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico
3Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autónoma Metropolitana, 09340 Mexico City, Mexico

Received 21 August 2015; Revised 18 November 2015; Accepted 22 November 2015

Academic Editor: Joao Cardoso

Copyright © 2016 Montserrat Alvarado-González et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.