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基于时滞反向最大信息系数模型的上肢皮质-肌肉耦合分析。

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

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2023;31:4635-4643. doi: 10.1109/TNSRE.2023.3334767. Epub 2023 Nov 30.

Abstract

In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG → EMG and EMG → EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation.

摘要

在肌肉骨骼系统中,准确描述生理电信号之间的耦合方向和强度至关重要。最大信息系数(MIC)可以有效地量化耦合强度,特别是对于短时间序列。然而,它无法识别信息传输的方向。本文通过引入时间延迟参数来测量因果耦合,提出了一种有效的时滞反向最大信息系数(TDBackMIC)分析方法。首先,在仿真中验证了 TDBackMIC 的有效性,然后将其应用于功能皮质-肌肉耦合和肌肉间耦合网络的分析,以探索不同握力强度下耦合特征的差异。实验结果表明,功能皮质-肌肉耦合和肌肉间耦合是双向的。在 10%最大自主收缩(MVC)条件下,β波段 EEG→EMG 和 EMG→EEG 的平均耦合强度分别为 0.86±0.04 和 0.81±0.05,20% MVC 条件下分别为 0.83±0.05 和 0.76±0.04,30% MVC 条件下分别为 0.76±0.03 和 0.73±0.04。随着握力的增加,β频带中功能皮质-肌肉耦合的强度降低,肌肉间耦合网络的连通性增强,信息交换更加紧密。结果表明,TDBackMIC 可以准确判断因果耦合关系,不同握力下的功能皮质-肌肉耦合和肌肉间耦合网络不同,为运动康复提供了一定的理论依据。

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