ArticleSensors (Basel, Switzerland)2024
An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.Biomimetics (Basel, Switzerland) · 2026Article
- Deception Detection from Five-Channel Wearable EEG on LieWaves: A Reproducible Baseline for Subject-Dependent and Subject-Independent Evaluation.Sensors (Basel, Switzerland) · 2026Article
- A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection.Entropy (Basel, Switzerland) · 2025Article
- A role for preparatory midfrontal theta in autism as revealed by a high executive load brain-computer interface reverse spelling task.Scientific reports · 2025Article
- Biomedical Signal Processing and Health Monitoring Based on Sensors.Sensors (Basel, Switzerland) · 2025Article
- An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.Biomimetics (Basel, Switzerland) · 2024Article
- Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Automatic Recognition of Multiple Emotional Classes from EEG Signals through the Use of Graph Theory and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.