Multivariable causal relationship method based on R-vine Copula transfer entropy

Abstract

According to the method, on the basis of the transfer entropy of a classical causal analysis method, a Copula function in statistics is combined and popularized to the transfer entropy in a high-dimensional form, and a regular vine Copula function is used for estimation, so that an effective new R-Vine Copula multivariable transfer entropy method is provided. Firstly, compared with an existing algorithm on a series of simulation data, the multivariable causal analysis performance of the method is verified. Secondly, the method is applied to multi-channel electromyographic signal analysis when different wrist movement tasks are executed. According to the application of the coupling relation between muscles, when the neuromuscular system executes different wrist movement tasks such as wrist extension and wrist radial deviation, two community structures which are related to a joint area and have a stable causal coupling relation are formed. The R-Vine Copula multivariable transfer entropy accurately deduces the complex causal coupling relationship, and has a good application value.

Description

technical field [0001] The invention belongs to the research field of nervous system motion control mechanism, and relates to R-Vine Copula, transfer entropy, degree in complex network, clustering coefficient, average distance, density, reciprocity and modularity calculation, thereby performing intermuscular coupling network analysis. Background technique [0002] At present, there are many methods for analyzing the causal coupling of two time series and multiple time series. The Granger causality (GC) analysis method based on the multivariate autoregressive model is relatively simple to calculate and can effectively describe the bidirectional connection in the network. , but it ignores the correlation of model coefficients and can only measure the linear causal relationship between time series. Shannon transfer entropy (STE) proposed by Schreiber et al., as an asymmetric extension form of mutual information (MI), itself contains causal direction and dynamic strength, and ha...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of high computational complexity and lack of convergence of the above causal methods, in order to better understand the coupling relationship between muscles, the present invention extends the transfer entropy to high-dimensional situations according to the principle of Copula entropy equivalence, and at the same time inherits many of the R-Vine Copula Advantages, a new method of R-Vine Copula multivariate transfer entropy is proposed

Images