title: Identification of common synaptic inputs to motor neurons from the rectified electromyogram.
abstract: Oscillatory common inputs of cortical or peripheral origin can be identified from the motor neuron output with coherence analysis. Linear transmission is possible despite the motor neuron non-linearity because the same input is sent commonly to several neurons. Because of the linear transmission, common input components to motor neurons can be investigated from the surface EMG, for example by EEG-EMG or EMG-EMG coherence. In these studies, there is an open debate on the utility and appropriateness of EMG rectification. The present study addresses this issue using an analytical, simulation and experimental approach. The main novel theoretical contribution that we report is that the spectra of both the rectified and the raw EMG contain input spectral components to motor neurons. However, they differ by the contribution of amplitude cancellation which influences the rectified EMG spectrum when extracting common oscillatory inputs. Therefore, the degree of amplitude cancellation has an impact on the effectiveness of EMG rectification in extracting input spectral peaks. The theoretical predictions were exactly confirmed by realistic simulations of a pool of motor neurons innervating a muscle in a cylindrical volume conductor of EMG generation and by experiments conducted on the first dorsal interosseous and the abductor pollicis brevis muscles of seven healthy subjects during pinching. It was concluded that when the contraction level is relatively low, EMG rectification may be preferable for identifying common inputs to motor neurons, especially when the energy of the action potentials in the low frequency range is low. Nonetheless, different levels of cancellation across conditions influence the relative estimates of the degree of linear transmission of oscillatory inputs to motor neurons when using the rectified EMG.
authors.affiliation: Department of Neurorehabilitation Engineering, Bernstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Von-Siebold-Str. 4,37075 Göttingen, Germany. dario.farina@bccn.uni-goettingen.de
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
authors.forename: Francesco
authors.initials: F
authors.lastname: Negro
authors.forename: Ning
authors.initials: N
authors.lastname: Jiang
mesh_terms: D000200:Action Potentials; D000328:Adult; D004576:Electromyography; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008297:Male; D008954:Models, Biological; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D009435:Synaptic Transmission; D055815:Young Adult