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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, M Mohajelin, F Khaleghi, N Sheykhivand, S Danishvar, S |
Keywords: | CNN;EEG;deep learning networks;lie detection |
Issue Date: | 3-Jun-2024 |
Publisher: | MDPI |
Citation: | Rahmani, M. et al. (2024) 'An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks', Sensors, 24 (11), 3598, pp. 1 - 17. doi: 10.3390/s24113598. |
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. |
Description: | Data Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data. |
URI: | https://bura.brunel.ac.uk/handle/2438/29706 |
DOI: | https://doi.org/10.3390/s24113598 |
Other Identifiers: | ORCiD: Sobhan Sheykhivand https://orcid.org/0000-0002-2275-8133 ORCiD: Sebelan Danishvar https://orcid.org/0000-0002-8258-0437 3598 |
Appears in Collections: | Dept of Computer Science Research Papers Dept of Civil and Environmental Engineering Research Papers |
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