An efficient Concealed Information Test: EEG feature extraction and ensemble classification for lie identification

EEG-based lie detectors have become popular over polygraphs because it cannot be controlled by human intentions. Various studies have performed “Guilty Knowledge Test” or “Concealed Information Test” by creating a mock crime scenario to identify changes in brain potential. In this study, an individual’s behavior during lying is analyzed and a new scenario is developed for “Concealed Information Test.” This work involves a mock crime scenario using an EEG acquisition device for 10 participants. Data acquisition has been performed by placing 16 electrodes on the subjects’ scalp. For this experiment, the subject has to recognize faces of some known and unknown personalities among 10 images flashed. These images behave as stimulus for the subject which generate corresponding brain responses. Various feature extraction approaches such as statistical, time domain, frequency domain and time–frequency domain are applied to the 16- channel EEG data. For classifying a subject as guilty or innocent, five classifiers have been applied on subject-wise EEG data. Moreover, the classifiers’ ranking is considered based on the performance of classifiers. An ensemble framework is developed by aggregating the results of the best three classifiers out of the tested five classifiers. The classifiers’ results are aggregated using a weighted voting approach and have been compared with popular conventional approaches using various classification performance measures. Results present a comparative performance of different feature extraction approaches and classifiers using subject-wise single-trial EEG data. The wavelet approach performs better for EEG data of most of the subjects. A comparison between base classifiers and ensemble framework is provided with the ensemble approach outperforming over the base classifiers. Further proposed framework is compared with some existing approaches, and a highest accuracy of 92.4% has been achieved.

[1]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[2]  J. Rosenfeld,et al.  Simple, effective countermeasures to P300-based tests of detection of concealed information. , 2004, Psychophysiology.

[3]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

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

[5]  Mohammad Hassan Moradi,et al.  Multimodal detection of concealed information using Genetic-SVM classifier with strict validation structure , 2017 .

[6]  Francisco Javier García Castellano,et al.  Expert Systems With Applications , 2022 .

[7]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[8]  B. Hjorth.  EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[9]  Bartosz Gula,et al.  Association-based Concealed Information Test: A Novel Reaction Time-Based Deception Detection Method , 2017 .

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

[11]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[12]  Amir Akhavan,et al.  Subject-based discriminative sparse representation model for detection of concealed information , 2017, Comput. Methods Programs Biomed..

[13]  Martin Buss,et al.  Feature Extraction and Selection for Emotion Recognition from EEG , 2014, IEEE Transactions on Affective Computing.

[14]  J Peter Rosenfeld,et al.  A mock terrorism application of the P300-based concealed information test. , 2011, Psychophysiology.

[15]  Stuart J. Russell,et al.  Artificial Intelligence , 1999 .

[16]  Daniel Svozil,et al.  Introduction to multi-layer feed-forward neural networks , 1997 .

[17]  Damodar Reddy Edla,et al.  Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification , 2018, J. Intell. Fuzzy Syst..

[18]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

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

[20]  Michael R. Winograd,et al.  The Complex Trial Protocol (CTP): a new, countermeasure-resistant, accurate, P300-based method for detection of concealed information. , 2008, Psychophysiology.

[21]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[22]  Hiroshi Nittono,et al.  The Role of Intention to Conceal in the P300-based Concealed Information Test , 2009, Applied psychophysiology and biofeedback.

[23]  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.

[24]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[25]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[26]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Liang Lu,et al.  A Novel Concealed Information Test Method Based on Independent Component Analysis and Support Vector Machine , 2012, Clinical EEG and neuroscience.

[28]  Athanasios V. Vasilakos,et al.  Brain computer interface: control signals review , 2017, Neurocomputing.

[29]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[30]  Damodar Reddy Edla,et al.  An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis , 2018 .