Intermuscular coupling network analysis of upper limbs based on R-vine copula transfer entropy

Abstract

<abstract> <p>In the field of neuroscience, it is very important to evaluate the causal coupling characteristics between bioelectrical signals accurately and effectively. Transfer entropy is commonly used to analyze complex data, especially the causal relationship between data with non-linear, multidimensional characteristics. However, traditional transfer entropy needs to estimate the probability density function of the variable, which is computationally complex and unstable. In this paper, a new and effective method for entropy transfer is proposed, by means of applying R-vine copula function estimation. The effectiveness of R-vine copula transfer entropy is first verified on several simulations, and then applied to intermuscular coupling analysis to explore the characteristics of the intermuscular coupling network of muscles in non-fatigue and fatigue conditions. The experiment results show that as the muscle group enters the fatigue state, the community structure can be adjusted and the muscle nodes participating in the exercise are fully activated, enabling the two-way interaction between different communities. Finally, it comes to the conclusion that the proposed method can make accurate inferences about complex causal coupling. Moreover, the characteristics of the intermuscular coupling network in both non-fatigue and fatigue states can provide a new theoretical perspective for the diagnosis of neuromuscular fatigue and sports rehabilitation, which has good application value.</p> </abstract>

Multi-Scale Characteristics of Investor Sentiment Transmission Based on Wavelet, Transfer Entropy and Network Analysis

“…Therefore, we apply CE to calculate TE. The application of CE to calculate TE has been discussed by previous literature [ 44 , 46 ] and has been adopted by research, such as [ 47 ]. Here, we connect TE and CE through the expressions below.…”

Upper Limb Cortical-Muscular Coupling Analysis Based on Time-Delayed Back Maximum Information Coefficient Model

“…If the threshold is too large, the intermuscular coupling network is too sparse and loses a lot of meaningful information. In this paper, according to the threshold selection [26], the threshold is set between 0.05 ∼ 0.95 *max(T DBackM IC), which is reduced by 0.05 *max(T DBackM IC) until there is no isolated node, where max(T DBackM IC) is the maximum value in the network. The adjacency matrix of intermuscular coupling network is D, and the threshold value is h, then the adjacency matrix A after thresholding is…”