CNV brain electricity lie detection method based on brain network analysis

Abstract

The invention discloses a CNV brain electrical lie detection method based on brain network analysis. The method comprises four parts of electroencephalogram signal acquisition, electroencephalogram signal preprocessing, electroencephalogram signal feature extraction and electroencephalogram signal display. The method uses a brain network analysis method to extract features of CNV brain waves, and makes up for the deficiency of existing lie detection technologies on cognitive difference of the whole brain region of electroencephalogram signals. Meanwhile, the electroencephalogram signals of multiple subjects in an experimental group and a control group are separately collected by software, the collected electroencephalogram signals are stored in a preset storage unit, pretreatment and feature extraction are conducted on electroencephalogram data of the same subject in different states, and analysis and comparison are conducted on the pretreatment and characteristics so as to provide reasonable lie detection results.

Description

technical field [0001] The invention belongs to the technical field of CNV electroencephalography polygraph research, and in particular relates to a CNV electroencephalographic polygraph method based on brain network analysis. Background technique [0002] The rapid development of science and technology in today's era, the intelligence of criminal tools and the concealment of criminal methods make it increasingly difficult to obtain evidence in cases. Therefore, the verification and identification of confessions has become the key to breakthroughs in cases. In recent years, psychological theory and scientific polygraph technology have begun to play an important role in interrogation practice and are widely used in business, politics, court trials, and national security. [0003] Event-related potential technology is one of the hotspots in polygraph technology research. It has the advantages of objectivity, stability, cost saving and non-invasiveness. It is mainly reflected i...

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Problems solved by technology

In addition, the researchers analyzed the difference between the two types of waveforms statistically, and the existing CNV EEG lie detection accuracy rate is about 80%.

[0005] In summary, the existing ERP lie detection methods mainly study the local lead characteristics of the EEG signal, while ignoring the cognitive differences of the lie detection EEG in the whole brain region.

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