title: A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM.
abstract: The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China; School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Junfeng
authors.initials: J
authors.lastname: Gao
authors.affiliation: Nanjing Fullshare Superconducting Technology Co., Ltd., Nanjing, People's Republic of China.
authors.forename: Hongjun
authors.initials: H
authors.lastname: Tian
authors.affiliation: School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, People's Republic of China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Department of Information Engineering, Officers College of CAPF, People's Republic of China.
authors.forename: Xiaolin
authors.initials: X
authors.lastname: Yu
authors.affiliation: College of Biomedical Engineering, South-Central University for Nationalities, Wuhan, People's Republic of China.
authors.forename: Chenhong
authors.initials: C
authors.lastname: Li
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, People's Republic of China.
authors.forename: Nini
authors.initials: N
authors.lastname: Rao
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D004569:Electroencephalography; D018913:Event-Related Potentials, P300; D005260:Female; D006801:Humans; D008014:Lie Detection; D008297:Male; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D055815:Young Adult