Electroencephalogram signal analysis method based on network motif

Abstract

The invention relates to an electroencephalogram signal analysis method which comprises the following steps: S1, constructing a functional brain network according to electroencephalogram signals; S2,according to the functional brain network, recognizing different network motifs, wherein the network motifs are in a node connection mode; S3, counting the number of different network motifs, and constructing a network motif characteristic vector; and S4, according to the network motif characteristic vector, completing intelligent classification of electroencephalograms. Compared with the prior art, the electroencephalogram signal analysis method provided by the invention is capable of effectively evaluating global connectivity of a brain function network, depicting function module division ofa bran, and accurately describing characteristics and states of the brain.

Description

technical field [0001] The invention belongs to the field of electroencephalogram signal analysis, in particular to an electroencephalogram signal analysis method based on a network phantom. Background technique [0002] Electroencephalogram (EEG) is a graph obtained by amplifying and recording the spontaneous biopotential of the brain from the scalp through sophisticated electronic instruments. It is the spontaneous and rhythmic electrical activity of brain cell groups recorded by electrodes. EEG signals have high time precision and can dynamically observe changes in the state of the brain, so EEG signals are an important tool for diagnosing different neurological disorders and diseases. The interaction and interconnection between neurons in the brain make the brain a complex dynamical system. Viewing the brain as a functional network and studying the temporal correlation between neural clusters in different regions of the brain to explore the connections between different...

AI Technical Summary

Problems solved by technology

At present, the establishment method of brain functional network is generally to establish a functional network based on the Pearson correlation coefficient of EEG signals collected between different electrodes, or to establish a functional network based on the phase-locked value of EEG signals between different electrodes, which ignores the The discharge characteristics of EEG signals at different moments, the established brain function network cannot better reflect the real state of the brain

Moreover, the existing research on the functional connectivity of brain neural networks is mainly based on degree distribution, clustering coefficient and shortest path to measure network functional connectivity. However, these measures mainly describe the local connectivity characteristics of brain neural networks, which cannot be effectively Measuring the global connectivity of the brain's neural network and not being able to describe the functional module division of the brain, so it also ignores some high-order hidden valuable information of the brain network

Images