Coupling analysis method of multi-channel electroencephalogram based on multi-scale multi-variate transfer entropy

Abstract

The invention relates to a coupling analysis method of multi-channel electroencephalogram based on multi-scale multi-variate transfer entropy and belongs to the technical field of non-linear dynamic causal systems and cerebral sensory motion network researches. The method comprises the following steps: 1, collecting a multi-channel electroencephalogram signal by adopting 32-channel Neuracle equipment; 2, respectively performing processing of removing baseline drift, electromyogram interference, eye movement interference and 50Hz power line interference on the collected electroencephalogram signal by adopting matlab software; 3, performing 20 different resolution decompositions on the multi-channel electroencephalogram signal by adopting a coarse-grained analysis method; and 4, analyzing the electroencephalogram signal under different resolutions among different time frequencies by adopting a multi-variate transfer entropy method, and quantitatively characterizing nonlinear coupling andinformation transfer characteristics among different cerebral sections. The coupling analysis method has the effects of describing the nonlinear characteristics among cerebral motion sensory cortex and deeply exploring the coupling intensity and information transfer among different regions of a brain.

Description

Technical field [0001] The invention relates to a multi-channel EEG coupling analysis method based on multi-scale and multi-variable transfer entropy, and belongs to the technical field of research on nonlinear dynamic causal systems and brain sensory motor networks. Background technique [0002] Exploring and quantifying the potential functional corticocortical connectivity (FCCC) between different brain regions in complex motor networks is an important topic. For a simple movement behavior, there is a coordination effect of multiple brain areas, so by analyzing the contribution of one brain area to another brain area, the interaction between complex motor networks can be obtained. At the same time, some physiological and biological systems exhibit complex operating mechanisms on multiple time scales. In recent years, the study of direct information interaction in complex brain networks has attracted more and more attention. At present, the FCCC in the left and right sensorimot...

AI Technical Summary

Problems solved by technology

In summary, the above methods mainly analyze the linear and nonlinear causal coupling relationship of bivariate systems. However, for complex brain network systems, there are correlation characteristics between different brain regions, and the common binary method is in The co-origin caused by the same reference electrode cannot be avoided when collecting scalp EEG signals; in addition, in biological and physiological systems, random processes on multiple time scales often have obvious or latent complex dynamics, so the proposed multi-scale and multi-variable It is necessary to use multiscale multivariate transfer entropy (MSMVTE) method to study the direct dynamic coupling characteristics of complex multivariate systems at different time scales.

Images