The mutual information analysis is a method based on information theory to describe the information interaction between two signals. In view of the difficulty in extracting features of EEG signals in the current lie detection method and the circumstance that the analysis of the overall cognitive function of the brain were increasingly important in brain cognitive science research

this paper applied the mutual information analysis method to the field of EEG lie detection for the first time and quantified the correlation between the brain nodes and perform statistical analysis on the calculation results. The mutual information of the electrode pairs with significant differences in the two groups were selected as the classification features

on which the pattern recognition was performed

resulting in the accuracy rate of 99.67%. This result proves that the mutual information analysis is an effective brain functional connection analysis method

which provides a new way for lie detection research based on EEG signal connection analysis.In addition

the brain function network of both lying and honest subjects was also analyzed. The results show that when lying

the frontal

parietal

temporal

and occipital regions of the brain cooperate to achieve the lie function

and in the connection between the brain regions corresponding to the physical behavior and other brain regions

significant differences between the two groups was also shown. These above results will help us further reveal the neural activity mechanism of the lie.