Open Access Open Access  Restricted Access Subscription Access

P300 Detection based on EEG Shape Features


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

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.
User
Notifications
Font Size

Abstract Views: 85

PDF Views: 1




  • P300 Detection based on EEG Shape Features

Abstract Views: 85  |  PDF Views: 1

Authors

Montserrat Alvarado-Gonzalez
Graduate Program in Computer Science and Engineering, Universidad Nacional Autonoma de Mexico, 04510 Mexico City, Mexico
Edgar Garduno
Department of Computer Science, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autonoma de Mexico, 04510 Mexico City, Mexico
Ernesto Bribiesca
Department of Computer Science, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autonoma de Mexico, 04510 Mexico City, Mexico
Oscar Yanez-Suarez
Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autonoma Metropolitana, 09340 Mexico City, Mexico
Veronica Medina-Banuelos
Neuroimaging Laboratory, Department of Electrical Engineering, Universidad Autonoma Metropolitana, 09340 Mexico City, Mexico

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.