A new approach for EEG feature extraction in P300-based lie detection.
P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods.
Abootalebi
Vahid
V
Electrical Engineering Department, Yazd University, Yazd, Iran. abootalebi@yazduni.ac.ir
Moradi
Mohammad Hassan
MH
Khalilzadeh
Mohammad Ali
MA
D000465:Algorithms; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008014:Lie Detection; D008297:Male