多导脑电复杂度特征的谎言测试研究

Study of Lie Detection Using Complexity Feature of Multichannel EEG

谎言测试在刑讯侦查和心理疾病治疗中具有重要意义。为了区分是否说谎,30名受试者被随机分为诚实和说谎两组,根据脑电信号的非线性特征——复杂性测度,对他们的12导联的脑电信号提取了KC复杂度、近似熵与样本熵3种复杂度特征,通过统计分析,用两类受试者具有显著差异的多导电极上的复杂度构建特征向量,最后使用支持向量机分类识别特征样本。研究发现:3种复杂度指标中,两类受试者的样本熵特征在更多电极上存在显著差异,由它们构建的特征向量的分类准确率最高,表明样本熵可以更有效地区分诚实和说谎两种不同脑认知状态下的脑电信号,该研究为基于脑电的测谎提供了一种新的途径。

There is great significance in lie detection for the criminal investigation and psychological disease treatment. To distinguish lying, thirty subjects were divided into lying and telling-truth groups randomly and three groups of nonlinear features--complexity measures including Kolmogorov complexity, approximate entropy and sample entropy were extracted. By statistical analysis, the feature vector was constructed by using complexity on the muilti electrodes with significant difference of complexity values between the two groups of subjects. The support vector machine was used to classify and idendify feature samples. The study finds that there are more electrodes with significant difference of complexity values for the sample entropy, and the highest classification accuracy can be observed for the feature vector constructed from the sample entropy, compared with the other two featues. Experimental resutls indicate that sample entropy could be used to classify EEG signals in lying from EEG signals in telling-truth, which provides a new alternative for EEG-based lie detection method.