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

Journal: Sensors (Basel, Switzerland)
PMID: 38894389

Abstract

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.

Authors

  • Mahsan Rahmani
    Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran.
  • Fatemeh Mohajelin
    Psychology Department, University of Aston, Birmangham B4 7ET, UK.
  • Nastaran Khaleghi
    Department of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran.
  • Sobhan Sheykhivand
    Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Electronic address: s.sheykhivand@tabrizu.ac.ir.
  • Sebelan Danishvar
    College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK.

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