{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T22:07:44Z","timestamp":1782511664606,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s24113598","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T05:58:00Z","timestamp":1717394280000},"page":"3598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks"],"prefix":"10.3390","volume":"24","author":[{"given":"Mahsan","family":"Rahmani","sequence":"first","affiliation":[{"name":"Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Mohajelin","sequence":"additional","affiliation":[{"name":"Psychology Department, University of Aston, Birmangham B4 7ET, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nastaran","family":"Khaleghi","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2275-8133","authenticated-orcid":false,"given":"Sobhan","family":"Sheykhivand","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Bonab, Bonab 55517-61167, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-0437","authenticated-orcid":false,"given":"Sebelan","family":"Danishvar","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6490","DOI":"10.1109\/JSEN.2023.3348661","article-title":"Human Activity Recognition Based on Wireless Electrocardiogram and Inertial Sensors","volume":"1","author":"Farrokhi","year":"2024","journal-title":"IEEE Sens. 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