P300 Detection Based on EEG Shape Features

Computational and Mathematical Methods in Medicine
Montserrat Alvarado-GonzálezVerónica Medina-Bañuelos

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.

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Citations

Jul 25, 2019·Frontiers in Computational Neuroscience·Rodrigo RameleJuan Miguel Santos
Nov 21, 2018·Brain Sciences·Rodrigo RameleJuan Miguel Santos
Jan 31, 2020·Frontiers in Computational Neuroscience·Dennis Joe HarmahPeng Xu
Apr 1, 2020·Cognitive Neurodynamics·Cili ZuoAndrzej Cichocki

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Software Mentioned

SWLDA
FeatureExtractor
AUROCEstimator

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