基于多尺度传递熵的脑肌电信号耦合分析

神经运动控制中脑肌电同步特征可以反映皮层与肌肉之间的功能联系. 为定量研究脑电和肌电信号在不同时间尺度上的同步耦合特征, 提出多尺度传递熵方法实现静态握力输出下的脑肌电耦合分析: 对同步采集的头皮脑电信号(EEG) 和表面肌电信号(EMG)进行多尺度化, 计算不同尺度因子下EEG与EMG间的传递熵值, 获取不同耦合方向(EEG→EMG及EMG→EEG)上的非线性脑肌电耦合特征; 进一步计算功能频段下的显著性面积指标, 定量分析不同尺度下皮层肌肉功能耦合强度的差异. 分析结果显示, 静态握力输出时beta频段(15–35 Hz)皮层肌肉功能耦合特征显著, 且beta2频段(25–35 Hz)在不同尺度上EEG→EMG方向的耦合强度大于EMG→EEG方向, 耦合强度最大值和方向间耦合强度差异显著值均出现于较高时间尺度. 研究结果揭示: 皮层肌肉功能耦合具有双向性, 且耦合强度在不同时间尺度和不同功能频段上有所差异, 可利用多尺度传递熵定量刻画大脑皮层与肌肉之间的非线性同步特征及功能联系.

Functional coupling analyses of electroencephalogram and electromyogram based on multiscale transfer entropy

Synchronization analyses of electroencephalogram (EEG) and electromyogram (EMG) could reveal the functional corticomuscular coupling (FCMC) between sensorimotor cortex and motor units firing in a target muscle. In order to quantitatively analyze the nonlinear functional coupling characteristics of EEG and EMG on a multiple time scale, a multiscale transfer entropy (MSTE) method based on the transfer entropy theory is proposed. Considering the multi-scale characteristics of EEG and EMG signals, the EEG and EMG signals are firstly decomposed into multiscale ones, respectively, to show the information on different time scales. Then the signals on different time scales are decomposed into different frequency bands to show the frequency domain characteristics. Finally, the EEG and EMG in different frequency bands on different scales are calculated by the MSTE method to obtain the FCMC characteristics on different time scales and in coupling frequency bands. In this study the MSTE is used to quantitatively analyze the nonlinear functional connection between EEG over the brain scalp and the surface EMG from the flexor digitorum surerficialis (FDS), which are recorded simultaneously during grip task with steady-state force output.#br#In the process of data processing, the coarse graining method is introduced firstly to decompose the EEG and EMG recorded in the task. Secondly, MSTEs between EEG and EMG on various scales are calculated to describe the nonlinear FCMC characteristics in different pathways (EEG→EMG and EMG→EEG). Furthermore, a significant indicator of MSTE is defined to quantitatively analyze the discrepancy between FCMC interaction strengths in the specific frequency band. The results show that the functional corticomuscular coupling is significant in both descending (EEG→EMG) and ascending (EMG→EEG) directions in the beta-band (15-35 Hz) in the static force output stage, especially that the interaction strength in descending direction is stronger in beta2-band (15-35 Hz) than that in the ascending direction. Meanwhile, the maximum FCMC strength value and the maximum or minimum discrepancy value between coupling directions on different scales almost occur on the high scales (15-30). Our study confirms that beta oscillations of EEG travel bidirectionally between the sensorimotor cortex and contralateral muscles in the sensorimotor loop system, and beta2 band is likely to reflect the motor control commands from the cortex to the muscle. Additionally, the discrepancy varies on different time scales and in different coupling frequency bands. The results show that the MSTE can quantitatively estimate the nonlinear interconnection and functional corticomuscular coupling between the sensorimotor cortex and the muscle.