School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Clin EEG Neurosci. 2012 Jan;43(1):54-63. doi: 10.1177/1550059411428715.
The concealed information test (CIT) has drawn much attention and has been widely investigated in recent years. In this study, a novel CIT method based on denoised P3 and machine learning was proposed to improve the accuracy of lie detection. Thirty participants were chosen as the guilty and innocent participants to perform the paradigms of 3 types of stimuli. The electroencephalogram (EEG) signals were recorded and separated into many single trials. In order to enhance the signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 components (i.e., artifacts) from every single trial. In order to automatically identify the P3 independent components (ICs), a new method based on topography template was proposed to automatically identify the P3 ICs. Then the P3 waveforms with high SNR were reconstructed on Pz electrodes. Second, the 3 groups of features based on time,frequency, and wavelets were extracted from the reconstructed P3 waveforms. Finally, 2 classes of feature samples were used to train a support vector machine (SVM) classifier because it has higher performance compared with several other classifiers. Meanwhile, the optimal number of P3 ICs and some other parameter values in the classifiers were determined by the cross-validation procedures. The presented method achieved a balance test accuracy of 84.29% on detecting P3 components for the guilty and innocent participants. The presented method improves the efficiency of CIT in comparison with previous reported methods.
隐藏信息测试（CIT）近年来受到了广泛关注和研究。本研究提出了一种新的基于去噪 P3 和机器学习的 CIT 方法，以提高测谎的准确性。选择 30 名参与者作为有罪和无罪参与者，执行 3 种刺激范式。记录脑电图（EEG）信号，并将其分为多个单试。为了提高 P3 成分的信噪比（SNR），采用独立成分分析（ICA）方法从每个单试中分离非 P3 成分（即伪迹）。为了自动识别 P3 独立成分（IC），提出了一种基于地形图模板的新方法来自动识别 P3 IC。然后，在 Pz 电极上重建具有高 SNR 的 P3 波形。其次，从重建的 P3 波形中提取基于时间、频率和小波的 3 组特征。最后，使用 2 类特征样本训练支持向量机（SVM）分类器，因为它比其他几种分类器具有更高的性能。同时，通过交叉验证程序确定分类器中的最优 P3 IC 数量和一些其他参数值。该方法在检测有罪和无罪参与者的 P3 成分时，平衡测试准确率达到 84.29%。与以前报道的方法相比，该方法提高了 CIT 的效率。
