Volume 49 Issue 1
Jan.  2023
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WEI S H,ZHANG J Q,LI F,et al. Lie mechanism based on phase transfer entropy of EEG signals[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):23-30 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0187
Citation: WEI S H,ZHANG J Q,LI F,et al. Lie mechanism based on phase transfer entropy of EEG signals[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):23-30 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0187

Lie mechanism based on phase transfer entropy of EEG signals

doi: 10.13700/j.bh.1001-5965.2021.0187
Funds:  National Natural Science Foundation of China (61773408); The Fundamental Research Funds for the Central Universities(CZZ19004,CZY20039)
More Information
  • Corresponding author: E-mail:Junfengmst@163.com
  • Received Date: 12 Apr 2021
  • Accepted Date: 31 May 2021
  • Available Online: 16 Jan 2023
  • Publish Date: 15 Jun 2021
  • Lying is a complex cognitive process whose executive function requires the participation of different brain regions. And the interaction between these brain regions has been confirmed by related research. In view of the problems of limited current EEG signal feature extraction methods and the unclear psychological mechanism of lying, we constructed EEG signals-based brain networks by phase transfer entropy during the lie experiment, and we have analyzed the effective connectivity between different brain regions in the honest group and the lying group. First, the standard three-stimulus experiment was used to conduct a lying detection experiment on 60 subjects. The EEG signals of all subjects were collected simultaneously and preprocessed.Then, the phase transfer entropy was used to construct the effective connectivity matrix. Subsequently, the statistical method was used to analyze the entropy value difference between the two groups of each edge in the matrix, and the electrode pairs with significant differences in entropy value were selected as the classification features of the fully connected neural network. The result shows that the classification accuracy rate is 96.75%, indicating that it is effective to use phase transfer entropy index to distinguish the EEG signals of the liar and the honest. Finally, the brain function network of the two groups of people was analyzed. The results show that compared with honest people, there is a stronger flow of information between the frontal, parietal and temporal lobes of the liar, indicating that deception requires coordination and utilization of more brain resources. The above analysis results will help reveal the brain’s neural activity mechanism in a lying state.

     

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