Guilty Knowledge Test by Chaotic Processing of Electroencephalogram Signals Based on Fuzzy Recurrence Plot

Document Type : Original Article

Authors

1 Master's student, Faculty of Modern Sciences and Technologies, Semnan University, Semnan, Iran

2 Assistant Professor, Faculty of Modern Sciences and Technologies, Semnan University, Semnan, Iran

3 Assistant Professor, Faculty of Medical Engineering, Hamedan University of Technology, Hamedan, Iran

Abstract

The EEG-based guilty knowledge test (GKT) is one of the most frequent lie detection methods. Recurrence plot analysis is a conventional chaotic signal processing method applied in different lie detection studies. One of the most important challenges of this method is selecting the appropriate threshold as the criterion of state recurrence in the phase space. Inappropriate selection of this threshold significantly affects the performance of this method. So in this study, the fuzzy recurrence plot is applied to overcome this challenge. This method is applied to transform EEG trials into grayscale texture images. Then, the gray-level co-occurrence matrix (GLCM) is used to extract the texture features from these images. Finally, The extracted features are classified using the K-NN classifier. The classification results of the 4-D feature vectors with 90% accuracy indicate the superiority of this method compared to the classic RQA method with 13-D feature vectors. This reduction in feature vector dimension improves the train and test speed and generalization of the KNN as a lazy learner. Moreover, the subject-based EEG-trial processing approach of this research eliminates the need for data set from various subjects and the only data set required to determine the sincerity of each subject is solely its own data set.

Keywords

Smiley face

References

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History

  • Receive Date: 05 July 2021
  • Revise Date: 09 May 2022
  • Accept Date: 09 August 2022
  • Publish Date: 23 September 2022

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