Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (20): 158-164.DOI: 10.3778/j.issn.1002-8331.1908-0133

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Recognition Research on Concealing Behavior in Psychological Test

ZHAO Tong, HUANG Zheng, WANG Xiuchao, LI Miao, ZHANG Yun, ZHENG Xiujuan, LIU Kai   

  1. 1.College of Electrical Engineering, Sichuan University, Chengdu 610065, China
    2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    3.School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
    4.Department of Military Medical Psychology, Air Force Medical University, Xi’an 710032, China
    5.School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
  • Online:2020-10-15 Published:2020-10-13



  1. 1.四川大学 电气工程学院,成都 610065
    2.中国科学院 沈阳自动化研究所 机器人学国家重点实验室,沈阳 110016
    3.中国科学院大学 计算机与控制学院,北京 100049
    4.中国人民解放军空军军医大学 军事医学心理学系,西安 710032
    5.西安交通大学 电子与信息工程学院,西安 710049


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



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