Reprint of "A new approach to analyze data from EEG-based concealed face recognition system".

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

[1]  H. Behnam,et al.  Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables , 2014, Cognitive Neurodynamics.

[2]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[3]  Roberto Hornero,et al.  Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer’s disease patients , 2008, Medical & Biological Engineering & Computing.

[4]  Lei Wang,et al.  EEG recurrence markers and sleep quality , 2013, Journal of the Neurological Sciences.

[5]  Jürgen Kurths,et al.  Order patterns recurrence plots in the analysis of ERP data , 2007, Cognitive Neurodynamics.

[6]  M. Ghoshunia,et al.  Phase space analysis of Event Related Potential during episodic memory retrieval , 2007 .

[7]  Ioannis Kalatzis,et al.  Design and implementation of an SVM-based computer classification system for discriminating depressive patients from healthy controls using the P600 component of ERP signals , 2004, Comput. Methods Programs Biomed..

[8]  J. Kurths,et al.  Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Seyedehsamaneh Shojaeilangari,et al.  A New Unsupervised Pre-processing Algorithm Based on Artificial Immune System for ERP Assessment in a P300-based GKT , 2012 .

[10]  Jiancheng Sun,et al.  Denoised P300 and machine learning-based concealed information test method , 2011, Comput. Methods Programs Biomed..

[11]  Abdollah Arasteh,et al.  A Novel Method Based on Empirical Mode Decomposition for P300-Based Detection of Deception , 2016, IEEE Transactions on Information Forensics and Security.

[12]  John Polich,et al.  P300 in Clinical Applications: Meaning, Method, and Measurement , 1991 .

[13]  Ewout H. Meijer,et al.  The P300 is sensitive to concealed face recognition. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[14]  J. Sleigh,et al.  Using the Hilbert–Huang transform to measure the electroencephalographic effect of propofol , 2012, Physiological measurement.

[15]  A. H. Mehrnam,et al.  Concealed face recognition analysis based on Recurrence Plots , 2011, 2011 18th Iranian Conference of Biomedical Engineering (ICBME).

[16]  Gunnar Blohm,et al.  A New Method for EEG-Based Concealed Information Test , 2013, IEEE Transactions on Information Forensics and Security.

[17]  Elif Derya íbeyli.  Lyapunov exponents/probabilistic neural networks for analysis of EEG signals , 2010 .

[18]  Ali Motie Nasrabadi,et al.  Recurrence Plots for Identifying Memory Components in Single-Trial EEGs , 2010, Brain Informatics.

[19]  E Donchin,et al.  The truth will out: interrogative polygraphy ("lie detection") with event-related brain potentials. , 1991, Psychophysiology.

[20]  B. Simon,et al.  Analyzing 13NN lung washout curves in the presence of intraregional nonuniformities. , 1994, Journal of applied physiology.

[21]  Jürgen Kurths,et al.  Brain signal analysis based on recurrences , 2009, Journal of Physiology-Paris.

[22]  C L Webber,et al.  Dynamical assessment of physiological systems and states using recurrence plot strategies. , 1994, Journal of applied physiology.

[23]  D. Alistair Steyn-Ross,et al.  Frontal-Temporal Synchronization of EEG Signals Quantified by Order Patterns Cross Recurrence Analysis During Propofol Anesthesia , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[24]  J. Zbilut,et al.  Recurrence quantification in epileptic EEGs , 2001 .

[25]  Norbert Marwan,et al.  How to Avoid Potential Pitfalls in Recurrence Plot Based Data Analysis , 2010, Int. J. Bifurc. Chaos.

[26]  Ying Liu,et al.  Independent component analysis of EEG signals , 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005..

[27]  Mohammad Hassan Moradi,et al.  A new approach for EEG feature extraction in P300-based lie detection , 2009, Comput. Methods Programs Biomed..

[28]  Ewout H. Meijer,et al.  Memory detection with the Concealed Information Test: a meta analysis of skin conductance, respiration, heart rate, and P300 data. , 2014, Psychophysiology.

[29]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[30]  Mohammad Hassan Moradi,et al.  A comparison of methods for ERP assessment in a P300-based GKT. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[31]  A. Nasrabadi,et al.  The impacts of hypnotic susceptibility on chaotic dynamics of EEG signals during standard tasks of Waterloo-Stanford Group Scale , 2013, Journal of medical engineering & technology.

[32]  D. Lykken.  The GSR in the detection of guilt. , 1959 .

[33]  J P Rosenfeld,et al.  P300 scalp amplitude distribution as an index of deception in a simulated cognitive deficit model. , 1999, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[34]  A. Ademoglu,et al.  Analysis of pattern reversal visual evoked potentials (PRVEPs) by spline wavelets , 1997, IEEE Transactions on Biomedical Engineering.

[35]  Elif Derya Übeyli.  Lyapunov exponents/probabilistic neural networks for analysis of EEG signals , 2010, Expert Syst. Appl..

[36]  H. Adeli,et al.  Fractality analysis of frontal brain in major depressive disorder. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.