Taha Bewar Neamat, Baykara Muhammet, Alakuş Talha Burak
Department of Software Engineering, Fırat University, Elazığ 23119, Türkiye.
Department of Software Engineering, Kırklareli University, Kırklareli 39100, Türkiye.
Brain Sci. 2025 May 18;15(5):519. doi: 10.3390/brainsci15050519.
Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related Potential (ERP) P300 component have gained prominence for identifying concealed information. This systematic review aims to evaluate recent studies (2017-2024) on EEG-based lie detection using ERP P300 responses, especially in relation to recognized and unrecognized face stimuli. The goal is to summarize commonly used EEG signal processing techniques, feature extraction methods, and classification algorithms, identifying those that yield the highest accuracy in lie detection tasks. This review followed PRISMA guidelines for systematic reviews. A comprehensive literature search was conducted using IEEE Xplore, Web of Science, Scopus, and Google Scholar, restricted to English-language articles from 2017 to 2024. Studies were included if they focused on EEG-based lie detection, utilized experimental protocols like Concealed Information Test (CIT), Guilty Knowledge Test (GKT), or Deceit Identification Test (DIT), and evaluated classification accuracy using ERP P300 components. CIT with ERP P300 was the most frequently employed protocol. The most used preprocessing method was Bandpass Filtering (BPF), and the Discrete Wavelet Transform (DWT) emerged as the preferred feature extraction technique due to its suitability for non-stationary EEG signals. Among classification algorithms, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN) were frequently utilized. These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. Combining advanced signal processing methods with machine learning and deep learning classifiers significantly improves performance. This review identifies the most effective methodologies and suggests that future research should focus on real-time applications, cross-individual generalization, and reducing system complexity to facilitate broader adoption.
测谎在安全、执法和临床评估等领域至关重要。传统方法存在可靠性问题且容易受到对策的影响。近年来，脑电图（EEG），特别是事件相关电位（ERP）的P300成分，在识别隐藏信息方面受到了关注。本系统综述旨在评估2017年至2024年期间关于使用ERP P300反应进行基于脑电图的测谎的最新研究，特别是与已识别和未识别的面部刺激相关的研究。目的是总结常用的脑电信号处理技术、特征提取方法和分类算法，确定那些在测谎任务中准确率最高的方法。本综述遵循系统评价的PRISMA指南。使用IEEE Xplore、科学网、Scopus和谷歌学术进行了全面的文献检索，仅限于2017年至2024年的英文文章。如果研究聚焦于基于脑电图的测谎，采用诸如隐蔽信息测试（CIT）、有罪知识测试（GKT）或欺骗识别测试（DIT）等实验方案，并使用ERP P300成分评估分类准确率，则纳入研究。使用ERP P300的CIT是最常用的方案。最常用的预处理方法是带通滤波（BPF），离散小波变换（DWT）由于其适用于非平稳脑电信号而成为首选的特征提取技术。在分类算法中，支持向量机（SVM）、线性判别分析（LDA）和卷积神经网络（CNN）被频繁使用。这些发现证明了基于混合和深度学习的模型在提高分类性能方面的有效性。与传统的测谎仪方法相比，基于脑电图的测谎，特别是使用ERP P300对面部识别任务的反应，显示出有前景的准确率和鲁棒性。将先进的信号处理方法与机器学习和深度学习分类器相结合可显著提高性能。本综述确定了最有效的方法，并建议未来的研究应专注于实时应用、跨个体泛化以及降低系统复杂性，以促进更广泛的应用。
