Cheng Shengcui, Chen Xiaoling, Zhang Yuanyuan, Wang Ying, Li Xin, Li Xiaoli, Xie Ping
Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei China.
Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei China.
Cogn Neurodyn. 2023 Dec;17(6):1575-1589. doi: 10.1007/s11571-022-09895-y. Epub 2022 Nov 24.
The multiscale information interaction between the cortex and the corresponding muscles is of great significance for understanding the functional corticomuscular coupling (FCMC) in the sensory-motor systems. Though the multiscale transfer entropy (MSTE) method can effectively detect the multiscale characteristics between two signals, it lacks in describing the local frequency-band characteristics. Therefore, to quantify the multiscale interaction at local-frequency bands between the cortex and the muscles, we proposed a novel method, named bivariate empirical mode decomposition-MSTE (BMSTE), by combining the bivariate empirical mode decomposition (BEMD) with MSTE. To verify this, we introduced two simulation models and then applied it to explore the FCMC by analyzing the EEG over brain scalp and surface EMG signals from the effector muscles during steady-state force output. The simulation results showed that the BMSTE method could describe the multiscale time-frequency characteristics compared with the MSTE method, and was sensitive to the coupling strength but not to the data length. The experiment results showed that the coupling at beta1 (15-25 Hz), beta2 (25-35 Hz) and gamma (35-60 Hz) bands in the descending direction was higher than that in the opposition, and at beta2 band was higher than that at beta1 band. Furthermore, there were significant differences at the low scales in beta1 band, almost all scales in beta2 band, and high scales in gamma band. These results suggest the effectiveness of the BMSTE method in describing the interaction between two signals at different time-frequency scales, and further provide a novel approach to understand the motor control.
The online version contains supplementary material available at 10.1007/s11571-022-09895-y.
皮层与相应肌肉之间的多尺度信息交互对于理解感觉运动系统中的功能性皮质-肌肉耦合(FCMC)具有重要意义。尽管多尺度转移熵(MSTE)方法可以有效检测两个信号之间的多尺度特征,但它缺乏对局部频带特征的描述。因此,为了量化皮层与肌肉在局部频带的多尺度交互,我们提出了一种新方法,称为双变量经验模式分解-MSTE(BMSTE),它将双变量经验模式分解(BEMD)与MSTE相结合。为了验证这一点,我们引入了两个仿真模型,然后将其应用于通过分析稳态力输出期间头皮脑电图和效应器肌肉的表面肌电图信号来探索FCMC。仿真结果表明,与MSTE方法相比,BMSTE方法能够描述多尺度时频特征,并且对耦合强度敏感而对数据长度不敏感。实验结果表明,下行方向β1(15 - 25Hz)、β2(25 - 35Hz)和γ(35 - 60Hz)频段的耦合高于相反方向,且β2频段高于β1频段。此外,β1频段的低尺度、β2频段的几乎所有尺度以及γ频段的高尺度存在显著差异。这些结果表明BMSTE方法在描述不同时频尺度下两个信号之间的交互方面是有效的,并进一步提供了一种理解运动控制的新方法。
在线版本包含可在10.1007/s11571-022-09895-y获取的补充材料。