Abootalebi Vahid, Moradi Mohammad Hassan, Khalilzadeh Mohammad Ali
Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran.
Int J Psychophysiol. 2006 Nov;62(2):309-20. doi: 10.1016/j.ijpsycho.2006.05.009. Epub 2006 Jul 24.
P300-based GKT (guilty knowledge test) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study is to evaluate three classifying methods for this approach and compare their performances in a lab analogue. Several subjects went through the designed GKT paradigm and their respective brain signals were recorded. For the analysis of signals, BAD (bootstrapped amplitude difference) and BCD (bootstrapped correlation difference) methods as two predefined methods alongside a new approach consisting of wavelet features and a statistical classifier were implemented. The rates of correct detection in guilty and innocent subjects were 74-80%. The results indicate the potential of P300-based GKT for detecting concealed information, although further research is required to increase its accuracy and precision and evaluating its vulnerability to countermeasures.
基于P300的有罪知识测试（GKT）已被提议作为传统测谎技术的替代方法。本研究的目的是评估该方法的三种分类方法，并在实验室模拟环境中比较它们的性能。几名受试者经历了设计好的GKT范式，并记录了他们各自的脑信号。为了进行信号分析，实施了BAD（自举幅度差）和BCD（自举相关差）这两种预定义方法，以及一种由小波特征和统计分类器组成的新方法。有罪和无罪受试者的正确检测率为74%-80%。结果表明基于P300的GKT在检测隐藏信息方面具有潜力，尽管需要进一步研究以提高其准确性和精确性，并评估其对反制措施的脆弱性。
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