A new approach for EEG feature extraction in P300-based lie detection

Authors:
Vahid Abootalebi;Mohammad Hassan Moradi;Mohammad Ali Khalilzadeh
Affiliations:
Electrical Engineering Department, Yazd University, Yazd, Iran and Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran and Research Center of Intelligent Signal Proces ...;Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran;Islamic Azad University, Mashhad Branch, Iran and Research Center of Intelligent Signal Processing, Tehran, Iran
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

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Abstract

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.