Volume 9, Issue 2 (3-2013)                   JSDP 2013, 9(2): 37-48 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mehrnam A H, Moti Nasr abadi A, Ghodousi M, Mohamadian A, Torabi S. Detection of Guilty Knowledge, Using Single Trial ERPs, and Based on Recurrence plots Nonlinear Method . JSDP. 2013; 9 (2) :37-48
URL: http://jsdp.rcisp.ac.ir/article-1-75-en.html
Abstract:   (11348 Views)
In this study, Recurrence Plots (RPs) has been adapted as a nonlinear approach in order to detection of guilty subjects’ knowledge based on their single-trial ERPs. The dataset were acquired 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 quantifiers were employed in feature extraction stage. Chaotic dynamic of brain’s signals and figuring out the trajectories in phase space are two important issues that can be indicated in these quantifiers. Results demonstrate, that the appearance of P300 signals in guilty subjects (because of denying a familiar face), 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: “inconstant threshold detection”, we achieved an accuracy about 89.7% on combining information of Fz, Cz and Pz channels.
Full-Text [PDF 2600 kb]   (1658 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2013/06/5 | Accepted: 2013/09/17 | Published: 2013/09/17 | ePublished: 2013/09/17

Add your comments about this article : Your username or Email:

Send email to the article author

© 2015 All Rights Reserved | Signal and Data Processing