Exploring the Functional Brain Network of Deception in Source-Level EEG via Partial Mutual Information

Abstract

In this study, partial mutual information at the source level was used to construct brain functional networks in order to examine differences in brain functions between lying and honest responses. The study used independent component analysis and clustering methods to computationally generate source signals from EEG signals recorded from subjects who were lying and those who were being honest. Partial mutual information was calculated between regions of interest (ROIs), and used to construct a functional brain network with ROIs as nodes and partial mutual information values as connections between them. The partial mutual information connections that showed significant differences between the two groups of people were selected as the feature set and classified using a functional connectivity network (FCN) classifier, resulting in an accuracy of 88.5%. Analysis of the brain networks of the lying and honest groups showed that, in the lying state, there was increased informational exchange between the frontal lobe and temporal lobe, and the language motor center of the frontal lobe exchanged more information with other brain regions, suggesting increased working and episodic memory load and the mobilization of more cognitive resources.

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks

“…The Method Used ACC (%) Abootalebi et al [9] P300 Waves 86 Amir et al [10] Classical Features 80 Mohammad et al [11] Brain Waves 79 Gao et al [12] SVM 96 Simbolon et al [13] ERP 83 Saini et al [14] SVM 98 Yohan et al [15] ANN 86 Bagel et al [16] CNN 84 Dodia et al [17] FFT-Hand Crafted Features 88 Kang et al [4] ICA + FCN 88.5 Boddu et al [6] PSO + SVM 96.45…”

“…However, despite its good performance, the polygraph is untrustworthy because experienced criminals can maintain normal physiological functions while being interrogated by the examiner with a polygraph and deceive both the examiner and the polygraph machine. As a result, the polygraph test results are not legal or valid [ 4 ]. However, in the recent decade, technologies beyond the polygraph, such as brain signals or electroencephalogram (EEG), have been created to identify truths and lies [ 5 , 6 ] accurately.…”

“…The maximum reported accuracy for the classification proposed by these researchers was 88%. Kang and colleagues [ 4 ] created a lie detection system using deep learning. This study employed independent component analysis (ICA) and clustering techniques.…”

7T Magnetic Compatible Multimodality Electrophysiological Signal Recording System

“…The main method of AAS to remove the gradient artifact is to construct the template of the gradient artifact and then subtract the artifact template from the original signal to obtain a clean EEG signal. In recent years, more and more machine learning and deep learning algorithms have been applied to EEG processing, as well as more and more research towards the automatic removal of gradient artifacts [21][22][23][24]. Duffy et al first used denoising autoencoders (DAE) to remove gradient artifacts for simultaneous EEG-fMRI automatically [25].…”