基于变分模态分解-传递熵的脑肌电信号耦合分析

皮层肌肉功能耦合是大脑皮层和肌肉组织间的相互作用, 脑肌电信号的多尺度耦合特征可以体现皮层-肌肉间多时空的功能联系. 本文引入变分模态分解并与传递熵结合, 构建变分模态分解-传递熵模型应用于脑肌间耦合研究. 首先基于变分模态分解将同步采集的脑电(EEG) 和肌电(EMG) 信号分别进行时频尺度化, 然后计算不同时频尺度间的传递熵值, 获取不同耦合方向(EEG→EMG 及EMG→EEG) 上不同尺度间的非线性耦合特征. 结果表明, 在静态握力输出条件下, 皮层与肌肉beta (15—35 Hz) 频段间的耦合强度最为显著; EEG→EMG 方向上脑电与肌电高gamma (50—72 Hz) 频段的耦合强度总体上高于EMG→EEG 方向.研究结果揭示皮层-肌肉功能耦合具有双向性, 且脑肌间不同耦合方向上、不同频段间的耦合强度有所差异.因此可利用变分模态分解-传递熵方法定量刻画大脑皮层与肌肉各时频段之间的非线性同步特征及功能联系.

Functional coupling analyses of electroencephalogram and electromyogram based on variational mode decomposition-transfer entropy

The functional corticomuscular coupling (FCMC) is defined as the interaction, coherence and time synchronism between cerebral cortex and muscle tissue, which could be revealed by the synchronization analyses of electroencephalogram (EEG) and electromyogram (EMG) firing in a target muscle. The FCMC analysis is an effective method to describe the information transfer and interaction in neuromuscular pathways. Forthermore, the multiscaled coherence analyses of EEG and EMG signals recorded simultaneously could describe the multiple spatial and temporal functional connection characteristics of FCMC, which could be helpful for understanding the multiple spatial and temporal coupling mechanism of neuromuscular system. In this paper, based on the adaptively decomposing signal into frequency band characteristis of variational mode decomposition (VMD) and the quantitatively detecting the directed exchange of information between two systems of transfer entropy (TE), a new method—variational mode decomposition-transfer entropy (VMD-TE) is proposed. The VMD-TE method could quantitatively analyze the nonlinear functional connection characteristic on multiple time-frequency scales between EEG over brain scalp and surface EMG signals from flexor digitorum surerficialis, which are recorded simultaneously during grip task with steady-state force output.In this paper, application of VMD-TE method consists of two steps. Firstly, the EEG and EMG signals are adaptively decomposed into multi intrinsic mode functions based on variational mode decomposition method, respectively, to describe the information on different time-frequency scales. Then the transfer entropies between the different timefrequency scales of EEG and EMG are calculated to describe the nonlinear corticomuscular coupling characteristic in different pathways (EEG→EMG and EMG→EEG), to show the functional coupling strength (namely VMD-TE values). finally, the maximum VMD-TE values between the different time-frequency scales of EEG and EMG signals among the eight subjects are selected, to describe the discrepancies of FCMC interaction strength between all time-frequency scales. The results show that 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. Meanwhile, the interaction strength between EEG signal and the gamma band (50-72 Hz) of EMG signal in descending direction is higher than in ascending direction. Our study confirms that the beta oscillations of EEG travel bidirectionally between sensorimotor