IEEE Trans Neural Syst Rehabil Eng. 2019 May;27(5):1092-1102. doi: 10.1109/TNSRE.2019.2907148. Epub 2019 Mar 25.
Functional corticomuscular coupling (FCMC) with different rhythmic oscillations plays different roles in neural communication and interaction between the central nervous system and the peripheral system. Larger methods, such as coherence and Granger causality (GC), have been used to describe the frequency band characteristics in the frequency domain, but they fail to account for the inherent complexity. Considering that the transfer entropy (TE) method as an information theory has advantages in complexity and direction, we extended it and proposed a novel method named transfer spectral entropy (TSE) to explore the local frequency band characteristics between two coupling signals. To verify this, we introduced a Henon model and a neural mass model to generate the simulation signals. We then applied the proposed method to explore the FCMC by analyzing the correlation between the EEG and EMG signals during steady-state force output. Simulation results showed that the TSE method, compared with the GC method, not only described the information interaction in the local frequency band but also restrained the "false coupling." In addition, the results also revealed that the TSE method was sensitive to coupling strength but not to the data length. Further analysis of the experimental data showed that beta1 (15-25 Hz) and beta2 (25-35 Hz) bands were prominent in the FCMC for both EEG-to-EMG and EMG-to-EEG directions. In addition, the statistical analysis of the significant area indicated that the coupling in the EEG-to-EMG direction was higher at the beta1 and beta2 bands than that in the EMG-to-EEG direction, and the coupling in the EMG-to-EEG direction was higher at the gamma1 band (35-45 Hz) than that in the opposition. The FCMC results complementarily refined the previous studies that mainly focused on the beta band (15-35 Hz). The simulation and experimental data expound the effectiveness of the TSE model to describe the information interaction in the local frequency band between two time series, and this study extends the relative studies on FCMC.
功能皮质肌耦合（FCMC）与不同的节律振荡在中枢神经系统和外周系统之间的神经通讯和相互作用中发挥不同的作用。较大的方法，如相干和格兰杰因果关系（GC），已被用于描述频域中的频带特征，但它们无法解释内在的复杂性。考虑到传递熵（TE）方法作为一种信息理论在复杂性和方向性方面具有优势，我们对其进行了扩展，并提出了一种新的方法，即传递谱熵（TSE），以探索两个耦合信号之间的局部频带特征。为了验证这一点，我们引入了 Henon 模型和神经质量模型来生成模拟信号。然后，我们通过分析稳态力输出期间 EEG 和 EMG 信号之间的相关性，应用所提出的方法来探索 FCMC。模拟结果表明，与 GC 方法相比，TSE 方法不仅描述了局部频带中的信息相互作用，而且还抑制了“虚假耦合”。此外，结果还表明，TSE 方法对耦合强度敏感，但对数据长度不敏感。对实验数据的进一步分析表明，在 EEG 到 EMG 和 EMG 到 EEG 方向上，FCMC 中β1（15-25 Hz）和β2（25-35 Hz）频段都很突出。此外，显著区域的统计分析表明，在 EEG 到 EMG 方向上的耦合在β1 和β2 频段比在 EMG 到 EEG 方向上的耦合更高，而在 EMG 到 EEG 方向上的耦合在γ1 频段（35-45 Hz）比在相反方向上的耦合更高。FCMC 的结果补充了以前主要集中在β频段（15-35 Hz）的研究。模拟和实验数据阐述了 TSE 模型描述两个时间序列之间局部频带内信息相互作用的有效性，并扩展了相对的 FCMC 研究。
