关键词: 掩饰行为, 非线性特征, 多维尺度分析（MDS）降维, 正则化核函数极限学习机

Abstract:

Electrocorticogram（EEG） can reveal people’s psychological activities correctly, so EEG is widely used in psychological test. In this paper, a method based on the nonlinear feature fusion of EEG signals for concealing behavior recognition in the psychological test is presented. First, after pre-processing the EEG signals of the subject obtained in the psychological test, four nonlinear features, including Lempel-Ziv Complexity（LZC）, Sample Entropy（SE）, Permutation Entropy（PE） and Fuzzy Entropy（FE）, are extracted. Then, the fusion and dimensionality reduction of the features by Multidimensional Scaling（MDS） is done. Finally, the Regularization Kernel Extreme Learning Machine（RKELM） is applied to construct the classifier and the performance of the four trained classifiers is verified by the test set. The experimental results show that the accuracy of the proposed system is 82.9%, which indicate the applicability of the proposed method.

Key words: concealing behavior, nonlinear feature, dimensionality reduction of multidimensional scaling, regularization kernel extreme learning machine