A Novel Concealed Information Test Method Based on Independent Component Analysis and Support Vector Machine

The concealed information test (CIT) has drawn much attention and has been widely investigated in recent years. In this study, a novel CIT method based on denoised P3 and machine learning was proposed to improve the accuracy of lie detection. Thirty participants were chosen as the guilty and innocent participants to perform the paradigms of 3 types of stimuli. The electroencephalogram (EEG) signals were recorded and separated into many single trials. In order to enhance the signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 components (ie, artifacts) from every single trial. In order to automatically identify the P3 independent components (ICs), a new method based on topography template was proposed to automatically identify the P3 ICs. Then the P3 waveforms with high SNR were reconstructed on Pz electrodes. Second, the 3 groups of features based on time, frequency, and wavelets were extracted from the reconstructed P3 waveforms. Finally, 2 classes of feature samples were used to train a support vector machine (SVM) classifier because it has higher performance compared with several other classifiers. Meanwhile, the optimal number of P3 ICs and some other parameter values in the classifiers were determined by the cross-validation procedures. The presented method achieved a balance test accuracy of 84.29% on detecting P3 components for the guilty and innocent participants. The presented method improves the efficiency of CIT in comparison with previous reported methods.