Lie detection using extreme learning machine: A concealed information test based on short‐time Fourier transform and binary bat optimization using a novel fitness function

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

In recent decades, many different governmental and nongovernmental organizations have used lie detection for various purposes, including ensuring the honesty of criminal confessions. As a result, this diagnosis is evaluated with a polygraph machine. However, the polygraph instrument has limitations and needs to be more reliable. This study introduces a new model for detecting lies using electroencephalogram (EEG) signals. An EEG database of 20 study participants was created to accomplish this goal. This study also used a six-layer graph convolutional network and type 2 fuzzy (TF-2) sets for feature selection/extraction and automatic classification. The classification results show that the proposed deep model effectively distinguishes between truths and lies. As a result, even in a noisy environment (SNR = 0 dB), the classification accuracy remains above 90%. The proposed strategy outperforms current research and algorithms. Its superior performance makes it suitable for a wide range of practical applications.

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

Brain-based concealed memory detection is driven mainly by orientation to salient items

In the pursuit of new methods for concealed memory detection, event-related potential components (ERP) have been placed at the forefront of research. No method, however, is scientifically complete without a theory and the present study therefore aimed to unravel the cognitive processes underlying these ERPs (i.e., orienting and arousal inhibition). This was accomplished by using a Concealed Information Test (CIT) in which participants were once motivated to conceal and once motivated to reveal their identity. The results showed a similarly strong P3 CIT effect in the two motivational conditions, which was enhanced for high salience compared to low salience identity items. Similar results were observed when using a multivariate machine-learning algorithm - suggesting that brain-based concealed memory detection is driven mainly by orientation to salient stimuli, rather than by arousal inhibition. In addition, the algorithm, trained and tested on the ERPs of different identity items, achieved detection rates exceeding those achieved by the P3. This implies that CIT researchers and practitioners could potentially rely on the entire ERP waveform instead of a-priori selecting separate components. Together these results enrich current understanding of the mechanisms underlying neurophysiological responding to concealed information and pave the way for novel and powerful algorithms which could be used in real-life forensic investigations.