Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity?
OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.
Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, Georg-August University of Göttingen, Göttingen, Germany.
Francesco
F
Negro
Kevin
K
Keenan
Dario
D
Farina
D000328:Adult; D000465:Algorithms; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D009469:Neuromuscular Junction; D009991:Oscillometry / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis / Q000379:methods; D009435:Synaptic Transmission / Q000502:physiology*