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Journal of Medical Systems
Published in: Journal of Medical Systems 4/2012

01-08-2012 | ORIGINAL PAPER

A New Approach for Concealed Information Identification Based on ERP Assessment

Authors: Min Zhao, Chongxun Zheng, Chunlin Zhao

Published in: Journal of Medical Systems | Issue 4/2012

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Abstract

Recently, numerous concealed information test (CIT) studies have been done with event related potential (ERP) for its sufficient validity in applied use. In this study, a new approach based on wavelet coefficients (WCs) and kernel learning algorithm is proposed to identify concealed information. Totally 16 subjects went through the designed CIT paradigm and the multichannel electroencephalogram (EEG) signals were recorded. Then, the high-dimensional WCs of ERP in delta, theta, alpha and beta rhythms were extracted. For the analysis of the data, kernel principle component analysis (KPCA) and a support vector machines (SVM) classifier are implemented. The results show that WCs features are significant differences between concealed information and irrelevant information (P < 0.05). The KPCA is able to effectively reduce feature dimensionalities and increase generalization performance of SVM. A high accuracy (93.6%) in recognizing concealed information and irrelevant information is achieved, which indicates the combination KPCA and SVM may provide a useful tool for detecting the concealed information.
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Metadata
Title
A New Approach for Concealed Information Identification Based on ERP Assessment
Authors
Min Zhao
Chongxun Zheng
Chunlin Zhao
Publication date
01-08-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 4/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9707-0

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