Negro Francesco, Keenan Kevin, Farina Dario
Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, Georg-August University of Göttingen, Göttingen, Germany.
J Neural Eng. 2015 Jun;12(3):036008. doi: 10.1088/1741-2560/12/3/036008. Epub 2015 Apr 27.
The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons.
We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals.
Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs.
This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.
在识别肌电图（EMG）信号功率谱中运动神经元的常见振荡输入之前，通常会先对EMG进行整流，以增强低频振荡成分。然而，整流是非线性算子，其对EMG信号频谱的影响尚未完全明确。在本研究中，我们旨在确定EMG整流在研究运动神经元振荡输入时何时有益。
我们对整流后的EMG信号功率谱以及运动单位动作电位平均形状对其的影响进行了完整的数学描述。我们还使用模拟和实验EMG信号对这些理论结果进行了验证。
使用先进计算模型的模拟和实验结果证明了关于整流对EMG频谱影响的理论推导的准确性。这些推导证明，当评估运动神经元低频（δ和α频段）常见突触输入的强度时、当动作电位持续时间较短时以及当抵消水平相对较低时，整流是有益的。另一方面，在研究较高频率（β和γ）时，整流可能会以取决于动作电位持续时间的方式扭曲对常见突触输入的估计，并且可能会在相干函数中引入与生理共享输入不对应的峰值。
本研究阐明了整流表面肌电图适用于研究神经连接性的条件。
