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Polygraph method based on multi-channel EEG signal Granger causality

A technology of EEG signals and EEG, applied in diagnostic recording/measurement, medical science, psychological devices, etc., can solve problems such as long experiment time, easy fatigue of subjects, and influence on test results

Inactive Publication Date: 2019-09-03
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

However, the current ERP-based lie detection method requires a large amount of data superposition, the experiment takes a long time, and the subjects are prone to fatigue. At the same time, it will also affect the test effect. The sensitivity of the

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  • Polygraph method based on multi-channel EEG signal Granger causality
  • Polygraph method based on multi-channel EEG signal Granger causality
  • Polygraph method based on multi-channel EEG signal Granger causality

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Embodiment Construction

[0043] see figure 1 , the present invention provides a polygraphy method based on EEG Granger causality, comprising the following steps:

[0044] 1) Through multi-lead EEG electrodes (N-lead EEG electrodes), the EEG signals of honest and lying subjects are extracted in real time, and the multi-lead EEG signals of the two types of subjects are respectively obtained and analyzed. save;

[0045] 2) Select the EEG signals of each lead of the subject obtained in step 1) and perform filtering, segmentation, baseline correction, artifact removal, and superimposed average preprocessing operations in sequence to obtain the corresponding lying stimuli of each lead of the two types of subjects. EEG signal; the filtering parameters of the present embodiment are set to be 0.05-30Hz band-pass filtering respectively; after filtering, the EEG data from 300ms before stimulation to 1300ms after stimulation are segmented as an epoch, and this epoch is called a P stimulus response; with The dat...

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Abstract

The invention discloses a polygraph method based on multi-channel EEG signal Granger causality. The method comprises the following steps: calculating the GC values between the respective lead EEG signals of the two types of subjects, generating a GC adjacency matrix of the two types of subjects; using the GC values of the electrode pairs with significant differences as classification characteristic, sending the GC values into the machine learning algorithm, performing the model training of machine learning, performing real time extraction on the EEG signals of the testers through the multi-lead EEG electrodes, obtaining the tester's multi-lead EEG signals and saving the signals, and performing respective pre-processing operations on the respective lead EEG signals, calculating the GC values between the tester's respective lead EEG signals, generating the tester's GC adjacency matrix, and sending the GC adjacency matrix as input to a trained classifier to obtain the polygraph result. The scheme is based on brain nerve signals, which greatly reduces the stimulation frequency required for testing, finally, after testing, the accuracy of lie detection is greatly improved.

Description

technical field [0001] The invention relates to the field of polygraph detection, in particular to a polygraphy method based on Granger causality of electroencephalogram signals. Background technique [0002] Lying is a common social and psychological phenomenon in human society. Lies have become a factor affecting social stability and unity, and pose a serious threat to the property and life safety of the people. Therefore, psychophysiologists and other related experts have been working hard to find effective lie detection methods. The effectiveness of lie detection has been confirmed in long-term and extensive application practice at home and abroad. First of all, polygraph technology has important application value for the detection of criminal investigation cases. In addition, lie recognition is also of great significance to the treatment of mental illness and mental disorders. In addition, the current international anti-terrorism situation is still grim, and our cou...

Claims

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Application Information

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IPC IPC(8): A61B5/16A61B5/0476A61B5/0478
CPCA61B5/164A61B5/291A61B5/369
Inventor 高军峰张家琦彭丝雨
Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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