EEG-based deception detection using weighted dual perspective visibility graph analysis

IF 3.1 3区 工程技术 Q2 NEUROSCIENCES Cognitive Neurodynamics Pub Date : 2024-09-13 DOI:10.1007/s11571-024-10163-4
Ali Rahimi Saryazdi, Farnaz Ghassemi, Zahra Tabanfar, Sheida Ansarinasab, Fahimeh Nazarimehr, Sajad Jafari
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Abstract

Deception detection is a critical aspect across various domains. Integrating advanced signal processing techniques, particularly in neuroscientific studies, has opened new avenues for exploring deception at a deeper level. This study uses electroencephalogram (EEG) signals from a balanced cohort of 22 participants, consisting of both males and females, aged between 22 and 29, engaged in a visual task for instructed deception. We propose a novel approach in the realm of deception detection utilizing the Weighted Dual Perspective Visibility Graph (WDPVG) method to decode instructed deception by converting average epochs from each EEG channel into a complex network. Six graph-based features, including average and deviation of strength, weighted clustering coefficient, weighted clustering coefficient entropy, average weighted shortest path length, and modularity, are extracted, comprehensively representing the underlying brain dynamics associated with deception. Subsequently, these features are employed for classification using three distinct algorithms: K Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). Experimental results reveal promising accuracy rates for KNN (66.64%), SVM (86.25%), and DT (82.46%). Furthermore, the features distributions of EEG networks are analyzed across different brain lobes, comparing truth-telling and lying modes. These analyses reveal the frontal and parietal lobes’ potential in distinguishing between truth and deception, highlighting their active role during deceptive behavior. The findings demonstrate the WDPVG method’s effectiveness in decoding deception from EEG signals, offering insights into the neural basis of deceptive behavior. This research could enhance the understanding of neuroscience and deception detection, providing a framework for future real-world applications.

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利用加权双视角可见度图分析进行基于脑电图的欺骗检测
欺骗检测是各个领域的一个重要方面。整合先进的信号处理技术,尤其是神经科学研究中的信号处理技术,为更深层次地探索欺骗开辟了新的途径。本研究使用了 22 名参与者的脑电图(EEG)信号,这些参与者中既有男性也有女性,年龄在 22 岁至 29 岁之间。我们在欺骗检测领域提出了一种新方法,即利用加权双视角可见性图(WDPVG)方法,通过将每个脑电图通道的平均时程转换成一个复杂的网络来解码指示欺骗。该方法提取了六个基于图的特征,包括强度的平均值和偏差、加权聚类系数、加权聚类系数熵、平均加权最短路径长度和模块化程度,全面反映了与欺骗相关的潜在大脑动态。随后,使用三种不同的算法对这些特征进行分类:K 近邻(KNN)、支持向量机(SVM)和决策树(DT)。实验结果表明,KNN(66.64%)、SVM(86.25%)和 DT(82.46%)的准确率很高。此外,还分析了不同脑叶的脑电图网络特征分布,比较了说真话和说谎模式。这些分析揭示了额叶和顶叶在区分真相和欺骗方面的潜力,突出了它们在欺骗行为中的积极作用。研究结果表明,WDPVG 方法能有效地从脑电信号中解码欺骗行为,从而深入了解欺骗行为的神经基础。这项研究可以加深人们对神经科学和欺骗检测的理解,为未来的实际应用提供一个框架。
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来源期刊
Cognitive Neurodynamics
Cognitive Neurodynamics 医学-神经科学
CiteScore
6.90
自引率
18.90%
发文量
140
审稿时长
12 months
期刊介绍: Cognitive Neurodynamics provides a unique forum of communication and cooperation for scientists and engineers working in the field of cognitive neurodynamics, intelligent science and applications, bridging the gap between theory and application, without any preference for pure theoretical, experimental or computational models. The emphasis is to publish original models of cognitive neurodynamics, novel computational theories and experimental results. In particular, intelligent science inspired by cognitive neuroscience and neurodynamics is also very welcome. The scope of Cognitive Neurodynamics covers cognitive neuroscience, neural computation based on dynamics, computer science, intelligent science as well as their interdisciplinary applications in the natural and engineering sciences. Papers that are appropriate for non-specialist readers are encouraged. 1. There is no page limit for manuscripts submitted to Cognitive Neurodynamics. Research papers should clearly represent an important advance of especially broad interest to researchers and technologists in neuroscience, biophysics, BCI, neural computer and intelligent robotics. 2. Cognitive Neurodynamics also welcomes brief communications: short papers reporting results that are of genuinely broad interest but that for one reason and another do not make a sufficiently complete story to justify a full article publication. Brief Communications should consist of approximately four manuscript pages. 3. Cognitive Neurodynamics publishes review articles in which a specific field is reviewed through an exhaustive literature survey. There are no restrictions on the number of pages. Review articles are usually invited, but submitted reviews will also be considered.
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