Data from: A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM

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.