title: P300 Detection Based on EEG Shape Features.
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 93%, that is, 10% 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 0.88. Also, most of the subjects needed less than 15 trials to have an AUROC superior to 0.8. Finally, we found that the electrode C4 also leads to better classification.
authors.affiliation: Graduate Program in Computer Science and Engineering, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico.
authors.forename: Montserrat
authors.initials: M
authors.lastname: Alvarado-González
authors.affiliation: Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico.
authors.forename: Edgar
authors.initials: E
authors.lastname: Garduño
authors.affiliation: Department of Computer Science, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, 04510 Mexico City, Mexico.
authors.forename: Ernesto
authors.initials: E
authors.lastname: Bribiesca
authors.affiliation: Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autónoma Metropolitana, 09340 Mexico City, Mexico.
authors.forename: Oscar
authors.initials: O
authors.lastname: Yáñez-Suárez
authors.affiliation: Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autónoma Metropolitana, 09340 Mexico City, Mexico.
authors.forename: Verónica
authors.initials: V
authors.lastname: Medina-Bañuelos
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D062207:Brain-Computer Interfaces; D002138:Calibration; D003198:Computer Simulation; D004566:Electrodes; D004569:Electroencephalography; D018913:Event-Related Potentials, P300; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D055815:Young Adult