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Amplitude cancellation influences the association between frequency components in the neural drive to muscle and the rectified EMG signal

机译:振幅消除会影响神经驱动肌肉的频率分量与整流的EMG信号之间的关联

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摘要

The rectified surface EMG signal is commonly used as an estimator of the neural drive to muscles and therefore to infer sources of synaptic input to motor neurons. Loss of EMG amplitude due to the overlap of motor unit action potentials (amplitude cancellation), however, may distort the spectrum of the rectified EMG and thereby its correlation with the neural drive. In this study, we investigated the impact of amplitude cancelation on this correlation using analytical derivations and a computational model of motor neuron activity, force, and the EMG signal. First, we demonstrated analytically that an ideal rectified EMG signal without amplitude cancellation (EMGnc) is superior to the actual rectified EMG signal as estimator of the neural drive to muscle. This observation was confirmed by the simulations, as the average coefficient of determination (r2) between the neural drive in the 1–30 Hz band and EMGnc (0.59±0.08) was matched by the correlation between the rectified EMG and the neural drive only when the level of amplitude cancellation was low (<40%) at low contraction levels (<5% of maximum voluntary contraction force; MVC). This correlation, however, decreased linearly with amplitude cancellation (r = -0.83) to values of r2 <0.2 at amplitude cancellation levels >60% (contraction levels >15% MVC). Moreover, the simulations showed that a stronger (i.e. more variable) neural drive implied a stronger correlation between the rectified EMG and the neural drive and that amplitude cancellation distorted this correlation mainly for low-frequency components (<5 Hz) of the neural drive. In conclusion, the results indicate that amplitude cancellation distorts the spectrum of the rectified EMG signal. This implies that valid use of the rectified EMG as an estimator of the neural drive requires low contraction levels and/or strong common synaptic input to the motor neurons.
机译:整流后的表面肌电信号通常用作估计神经对肌肉的驱动力,因此可以推断出运动神经元的突触输入源。然而,由于运动单元动作电位的重叠(幅度抵消)而导致的EMG振幅损失可能会使整流的EMG的频谱失真,从而使其与神经驱动器相关。在这项研究中,我们使用分析性推导和运动神经元活动,力和EMG信号的计算模型,研究了振幅抵消对这种相关性的影响。首先,我们通过分析证明,作为对肌肉神经驱动的估计器,没有幅度消除(EMGnc)的理想整流EMG信号要优于实际整流EMG信号。模拟结果证实了这一观察结果,因为1-30 Hz频带中的神经驱动与EMGnc(0.59±0.08)之间的平均确定系数(r 2 )与仅在低收缩水平(<最大自愿收缩力的5%; MVC)下,振幅消除水平低(<40%)时,才能纠正EMG和神经驱动。但是,在振幅抵消水平> 60%(收缩水平> 15%MVC)时,随着振幅抵消(r = -0.83),该相关性线性降低至r 2 <0.2。此外,仿真表明,较强的(即,可变性更大的)神经驱动暗示着经整流的肌电图和神经驱动之间的较强的相关性,并且振幅消除使该相关性主要针对神经驱动的低频分量(<5 Hz)而失真。总之,结果表明,幅度抵消使整流后的EMG信号的频谱失真。这意味着将整流后的肌电图有效地用作神经驱动器的估计器需要低收缩水平和/或向运动神经元的强烈突触输入。

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