TV-NARX and Coiflets WPT based time-frequency Granger causality with application to corticomuscular coupling in hand-grasping

Author:

Zhu Feifei,Li Yurong,Shi Zhengyi,Shi Wuxiang

Abstract

The study of the synchronous characteristics and functional connections between the functional cortex and muscles of hand-grasping movements is important in basic research, clinical disease diagnosis and rehabilitation evaluation. The electroencephalogram (EEG) and electromyographic signal (EMG) signals of 15 healthy participants were used to analyze the corticomuscular coupling under grasping movements by holding three different objects, namely, card, ball, and cup by using the time-frequency Granger causality method based on time-varying nonlinear autoregressive with exogenous input (TV-NARX) model and Coiflets wavelet packet transform. The results show that there is a bidirectional coupling between cortex and muscles under grasping movement, and it is mainly reflected in the beta and gamma frequency bands, in which there is a statistically significant difference (p < 0.05) among the different grasping actions during the movement execution period in the beta frequency band, and a statistically significant difference (p < 0.1) among the different grasping actions during the movement preparation period in the gamma frequency band. The results show that the proposed method can effectively characterize the EEG-EMG synchronization features and functional connections in different frequency bands during the movement preparation and execution phases in the time-frequency domain, and reveal the neural control mechanism of sensorimotor system to control the hand-grasping function achievement by regulating the intensity of neuronal synchronization oscillations.

Publisher

Frontiers Media SA

Subject

General Neuroscience

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Modeling and Causality Analysis of Human Sensorimotor Control System Based on NVAR Method;International Journal of Network Dynamics and Intelligence;2023-12-21

2. Decoding Natural Grasping Behaviors: Insights Into MRCP Source Features and Coupling Dynamics;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

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