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

Abstract

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 subjectwise 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.

A Systematic Literature Review on Machine Learning Algorithms for Human Status Detection

“…For enhancing classification, aggregating classifiers can be used as a modern and popular approach. Boosting, in which each classifier complements the previous one, voting, which is the most basic and popular, and stacking, in which the input to each meta-classifier is output from the preceding classifier are all examples of com-bination tactics [47]. Basic classification algorithms generate a single model from training data.…”

Epileptic seizure endorsement technique using DWT power spectrum

“…EEG-based lie detectors became common over polygraphs as a result of human actions cannot influence them. varied studies conducted "Guilty data Test" or "Concealed data Test" by making a mock crime state of affairs to spot potential changes within the brain [14]. This work enclosed a simulated crime state of affairs EEG acquisition tool for ten participants.…”

“…A comparison between the bottom classifiers and therefore the ensemble structure was given with the over-performing ensemble approach across the fundamental classifiers. The additional projected framework was compared with some existing approaches, achieving the most accuracy of 92.4% [14].…”

Machine Learning Mega-Analysis Applied to the Response Time Concealed Information Test: No Evidence for Advantage of Model-Based Predictors Over Baseline

“…Within forensics, one very practical ML application is to improve the prediction accuracy of computerized deception detection methods. There is increasing interest in this area, with most ML studies utilizing complex large data sets such as those from EEG and fMRI testings (e.g., Bablani et al, 2019;Davatzikos et al, 2005;Derakhshan et al, 2020;Dodia et al, 2019). However, ML can also be applied to comparatively simple data such as behavioral response times (RTs).…”