基于相位延迟指数的脑功能网络及测谎研究

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.

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

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.