标题： 多尺度小波转移熵及其在皮层肌肉耦合分析中的应用 显示英文标题

标题： Multiscale Wavelet Transfer Entropy with Application to Corticomuscular Coupling Analysis

摘要： 目标：运动皮层与肌肉活动之间的功能耦合通常通过皮层-肌肉相干性（CMC）或格兰杰因果分析（GC）来检测和量化，这些方法仅适用于线性耦合，并且不够敏感：一些健康的受试者没有显著的CMC和GC，但仍有良好的运动技能。 本工作的目标是开发具有改进灵敏度的功能性皮层-肌肉耦合测量方法，并能够检测线性和非线性相互作用。 方法：提出了一种多尺度小波转移熵（TE）方法。 该方法依赖于二进制平稳小波变换，将脑电图（EEG）和肌电图（EMG）信号分解为神经振荡的功能频段。 然后，它基于一系列嵌入延迟向量应用TE分析，以在不同时间尺度上检测和量化同一频率带和跨频率带的皮层-肌肉耦合。 结果：我们对神经生理信号的实验验证了所开发方法在检测和量化EEG和EMG信号之间信息流方面的潜力，适用于有或没有显著CMC或GC的受试者，包括非线性跨频率相互作用以及不同时间尺度上的相互作用。 获得的结果与潜在的感官运动神经生理学一致。 结论：这些发现表明，多尺度小波TE的概念提供了一个全面的框架，用于分析皮层-肌肉相互作用。 意义： 所提出的方法将有助于开发关于运动控制和更一般的神经生理过程的新见解。 显示英文摘要

摘要： Objective: Functional coupling between the motor cortex and muscle activity is commonly detected and quantified by cortico-muscular coherence (CMC) or Granger causality (GC) analysis, which are applicable only to linear couplings and are not sufficiently sensitive: some healthy subjects show no significant CMC and GC, and yet have good motor skills. The objective of this work is to develop measures of functional cortico-muscular coupling that have improved sensitivity and are capable of detecting both linear and non-linear interactions. Methods: A multiscale wavelet transfer entropy (TE) methodology is proposed. The methodology relies on a dyadic stationary wavelet transform to decompose electroencephalogram (EEG) and electromyogram (EMG) signals into functional bands of neural oscillations. Then, it applies TE analysis based on a range of embedding delay vectors to detect and quantify intra- and cross-frequency band cortico-muscular coupling at different time scales. Results: Our experiments with neurophysiological signals substantiate the potential of the developed methodologies for detecting and quantifying information flow between EEG and EMG signals for subjects with and without significant CMC or GC, including non-linear cross-frequency interactions, and interactions across different temporal scales. The obtained results are in agreement with the underlying sensorimotor neurophysiology. Conclusion: These findings suggest that the concept of multiscale wavelet TE provides a comprehensive framework for analysing cortex-muscle interactions. Significance: The proposed methodologies will enable developing novel insights into movement control and neurophysiological processes more generally.