Detection of Guilty Knowledge, Using Single Trial ERPs, Based on Non-linear method of Recurrence plots

In this study, Recurrence Plots (RPs) has been applied as a nonlinear approach in order to detection of guilty subjects’ knowledge based on their single-trial ERPs. The dataset were recorded from 49 human subjects who were participated in a Concealed Information Test (CIT). According to the test protocol, guilty subjects denied their information about familiar faces, so the aim was to detect the concealed faces in these subjects. Recurrence Quantification Analysis (RQAs) were employed in feature extraction stage. Brain's dynamic and it's trajectories in phase space are two important issues that can be indicated in these measures. Results demonstrate, that the appearance of P300 signals in guilty subjects (because of denying a familiar face), can increase the determinism and predictability of their brain’s signals. Also using Genetic Algorithm (GA) in feature selection level together with Linear Discriminant Analysis (LDA) classifier and a new method named: “variable threshold”, we achieved an accuracy about 89.7% on combining information of Fz, Cz and Pz channels.