Research on Brain Functional Network and Lie Detection Based on Phase Lag Index

SI Hui-fang; XIE Tian; GAO Jun-feng; GUAN Jin-an; XIANG Zhou-zhou; LAN Chang-you; Qing Xun-hua

doi:10.3969/j.issn.0372-2112.2018.07.029

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Research on Brain Functional Network and Lie Detection Based on Phase Lag Index

更新时间：2025-07-16

- Research on Brain Functional Network and Lie Detection Based on Phase Lag Index
- Acta Electronica Sinica Vol. 46, Issue 7, Pages: 1742-1747(2018)
- 作者机构：
  
  1. 中南民族大学生物医学工程学院认知科学国家民委重点实验室,湖北,武汉,430074
  2. 电子科技大学生命科学与技术学院,四川,成都,610054
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.81271659, No.61773408, No.91120017);Fundamental Research Funds for the Central Universities in 2017 (No.CZP17033);China Postdoctoral Science Foundation (No.2014M552346)
- DOI：10.3969/j.issn.0372-2112.2018.07.029
  CLC： R338
- Published Online：25 July 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
SI Hui-fang, XIE Tian, GAO Jun-feng, et al. Research on Brain Functional Network and Lie Detection Based on Phase Lag Index[J]. Acta Electronica Sinica, 2018, 46(7): 1742-1747.
DOI：

SI Hui-fang, XIE Tian, GAO Jun-feng, et al. Research on Brain Functional Network and Lie Detection Based on Phase Lag Index[J]. Acta Electronica Sinica, 2018, 46(7): 1742-1747. DOI： 10.3969/j.issn.0372-2112.2018.07.029.

摘要

在脑认知科学领域，越来越多的研究开始专注于利用不同导联脑电信号之间的相互依赖关系来研究大脑整体认知功能.相位延迟指数可有效减少由容积导体引起的误差，该方法已被广泛应用.而基于图论的脑网络研究方法在测谎方面还少见报道.本文通过对30名（诚实和说谎）受试者的脑电信号进行网络拓扑分析，将网络参数作为判别指标，使用支持向量机对实验数据进行分类.研究发现，两组受试者的小世界指标表现出显著的统计学差异，且得到较高的测谎准确率，结果证明了利用相位延迟指数方法进行图论分析的测谎有效性.

Abstract

In the field of brain cognitive science

more researches begin to focus on the interdependence between different leads of EEG signals to study the overall cognitive function of the brain.The Phase Lag Index (PLI) can reduce the errors effectively caused by the volume conduction and has been widely adopted

however brain network research method based on graph theory was scarcely reported in lie detection field.In this study

the network topology of the EEG signals from 30 (innocent and guilty) subjects are analyzed.The network parameters are used as the discriminant indicators

and the experimental data are classified by using support vector machine.The study finds that the small world indexes have pretty significant statistical differences between two groups.Also

the classification system gets a higher lie-detection accuracy

which proves the validity of polygraph using PLI method and graph theory analysis.

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