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- Title
An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.
- Authors
Rahmani, Mahsan; Mohajelin, Fatemeh; Khaleghi, Nastaran; Sheykhivand, Sobhan; Danishvar, Sebelan
- 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.
- Subjects
LIE detectors &; detection; FUZZY sets; ELECTROENCEPHALOGRAPHY; NONGOVERNMENTAL organizations; AUTOMATIC classification; FEATURE selection; DEEP learning
- Publication
Sensors (14248220), 2024, Vol 24, Issue 11, p3598
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s24113598