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

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.

[1]  D. Farina,et al.  Coherence of the Surface EMG and Common Synaptic Input to Motor Neurons , 2018, Front. Hum. Neurosci..

[2]  S. Gandevia,et al.  Accurate and representative decoding of the neural drive to muscles in humans with multi‐channel intramuscular thin‐film electrodes , 2015, The Journal of physiology.

[3]  J. Wessberg,et al.  Organization of motor output in slow finger movements in man. , 1993, The Journal of physiology.

[4]  Simon F Farmer,et al.  On the Need for Rectification of Surface Emg , 2022 .

[5]  J. Jakobi,et al.  Sex differences in force steadiness in three positions of the forearm , 2010, European Journal of Applied Physiology.

[6]  P. Mair,et al.  The potential use of spectral electromyographic fatigue as a screening and outcome monitoring tool of sarcopenic back muscle alterations , 2014, Journal of NeuroEngineering and Rehabilitation.

[7]  M. Gorassini,et al.  Changes in cortically related intermuscular coherence accompanying improvements in locomotor skills in incomplete spinal cord injury. , 2006, Journal of neurophysiology.

[8]  Andrew J Fuglevand,et al.  Distinguishing intrinsic from extrinsic factors underlying firing rate saturation in human motor units. , 2015, Journal of neurophysiology.

[9]  Dario Farina,et al.  Factors Influencing the Estimates of Correlation between Motor Unit Activities in Humans , 2012, PloS one.

[10]  A F Kohn,et al.  The amplitude and phase responses of the firing rates of some motoneuron models. , 2000, Bio Systems.

[11]  Martin Lakie,et al.  A dominant role for mechanical resonance in physiological finger tremor revealed by selective minimization of voluntary drive and movement. , 2013, Journal of neurophysiology.

[12]  V. Jousmäki,et al.  Task‐dependent modulation of 15‐30 Hz coherence between rectified EMGs from human hand and forearm muscles , 1999, The Journal of physiology.

[13]  C. Moritz,et al.  Discharge rate variability influences the variation in force fluctuations across the working range of a hand muscle. , 2005, Journal of neurophysiology.

[14]  G. Anrep,et al.  The influence of the vagus on pancreatic secretion , 1914, The Journal of physiology.

[15]  D. Farina,et al.  The optimal neural strategy for a stable motor task requires a compromise between level of muscle cocontraction and synaptic gain of afferent feedback. , 2015, Journal of neurophysiology.

[16]  Emilio Bizzi,et al.  Combinations of muscle synergies in the construction of a natural motor behavior , 2003, Nature Neuroscience.

[17]  Rogério Rodrigues Lima Cisi,et al.  Simulation system of spinal cord motor nuclei and associated nerves and muscles, in a Web-based architecture , 2008, Journal of Computational Neuroscience.

[18]  D. Farina,et al.  Fluctuations in isometric muscle force can be described by one linear projection of low‐frequency components of motor unit discharge rates , 2009, The Journal of physiology.

[19]  S. Baker,et al.  Beta-band intermuscular coherence: a novel biomarker of upper motor neuron dysfunction in motor neuron disease , 2012, Brain : a journal of neurology.

[20]  Minoru Shinohara,et al.  Muscle activation and time to task failure differ with load type and contraction intensity for a human hand muscle , 2005, Experimental Brain Research.

[21]  R. Merletti,et al.  Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle , 2011, Journal of neural engineering.

[22]  J. Rothwell,et al.  Cortical correlate of the Piper rhythm in humans. , 1998, Journal of neurophysiology.

[23]  Dario Farina,et al.  Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity? , 2015, Journal of neural engineering.

[24]  Christopher M Laine,et al.  Intermuscular coherence reflects functional coordination. , 2017, Journal of neurophysiology.

[25]  C. Christakos,et al.  Coherent motor unit rhythms in the 6-10 Hz range during time-varying voluntary muscle contractions: neural mechanism and relation to rhythmical motor control. , 2008, Journal of neurophysiology.

[26]  H. Clamann Statistical analysis of motor unit firing patterns in a human skeletal muscle. , 1969, Biophysical journal.

[27]  O. A. Nikitin,et al.  Neither high-pass filtering nor mathematical differentiation of the EMG signals can considerably reduce cross-talk. , 2002, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[28]  R. Enoka,et al.  Influence of amplitude cancellation on the simulated surface electromyogram. , 2005, Journal of applied physiology.

[29]  Neha Lodha,et al.  Force Control Is Related to Low-Frequency Oscillations in Force and Surface EMG , 2014, PloS one.

[30]  R. Enoka,et al.  Motor unit physiology: Some unresolved issues , 2001, Muscle & nerve.

[31]  Dario Farina,et al.  The effective neural drive to muscles is the common synaptic input to motor neurons , 2014, The Journal of physiology.

[32]  Dario Farina,et al.  Motor Neuron Pools of Synergistic Thigh Muscles Share Most of Their Synaptic Input , 2015, The Journal of Neuroscience.

[33]  D. Halliday,et al.  Rectification of EMG in low force contractions improves detection of motor unit coherence in the beta-frequency band. , 2013, Journal of neurophysiology.

[34]  K. Newell,et al.  Independence between the amount and structure of variability at low force levels , 2006, Neuroscience Letters.

[35]  R. Oostenveld,et al.  The α-motoneuron pool as transmitter of rhythmicities in cortical motor drive , 2010, Clinical Neurophysiology.

[36]  R. Enoka,et al.  Rate coding is compressed but variability is unaltered for motor units in a hand muscle of old adults. , 2007, Journal of neurophysiology.

[37]  R. Enoka,et al.  Influence of load type on presynaptic modulation of Ia afferent input onto two synergist muscles , 2009, Experimental Brain Research.

[38]  D. Farina,et al.  Linear transmission of cortical oscillations to the neural drive to muscles is mediated by common projections to populations of motoneurons in humans , 2011, The Journal of physiology.

[39]  J. Saunders,et al.  Relation of human electromyogram to muscular tension. , 1952, Electroencephalography and clinical neurophysiology.

[40]  D. Farina,et al.  Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation , 2016, Journal of neural engineering.

[41]  Richard R Neptune,et al.  Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. , 2010, Journal of neurophysiology.

[42]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[43]  D. Winter,et al.  Models of recruitment and rate coding organization in motor-unit pools. , 1993, Journal of neurophysiology.

[44]  F Lacquaniti,et al.  Neuromuscular adjustments of gait associated with unstable conditions. , 2015, Journal of neurophysiology.

[45]  Lena H Ting,et al.  Neuromechanics of muscle synergies for posture and movement , 2007, Current Opinion in Neurobiology.

[46]  Dario Farina,et al.  Motor unit recruitment strategies and muscle properties determine the influence of synaptic noise on force steadiness. , 2012, Journal of neurophysiology.

[47]  Dario Farina,et al.  Identification of common synaptic inputs to motor neurons from the rectified electromyogram , 2013, The Journal of physiology.

[48]  J. B. Nielsen,et al.  Coupling of antagonistic ankle muscles during co-contraction in humans , 2002, Experimental Brain Research.

[49]  S J Day,et al.  Experimental simulation of cat electromyogram: evidence for algebraic summation of motor-unit action-potential trains. , 2001, Journal of neurophysiology.

[50]  P. Matthews Relationship of firing intervals of human motor units to the trajectory of post‐spike after‐hyperpolarization and synaptic noise. , 1996, The Journal of physiology.

[51]  H B Boom,et al.  The median frequency of the surface EMG power spectrum in relation to motor unit firing and action potential properties. , 1992, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[52]  C. D. De Luca,et al.  Control scheme governing concurrently active human motor units during voluntary contractions , 1982, The Journal of physiology.

[53]  S. Baker,et al.  Intermuscular Coherence in Normal Adults: Variability and Changes with Age , 2016, PloS one.

[54]  L.H. Lindstrom,et al.  Interpretation of myoelectric power spectra: A model and its applications , 1977, Proceedings of the IEEE.

[55]  R. Enoka,et al.  Detecting the unique representation of motor-unit action potentials in the surface electromyogram. , 2008, Journal of neurophysiology.

[56]  Kevin C. McGill,et al.  EMGLAB: An interactive EMG decomposition program , 2005, Journal of Neuroscience Methods.

[57]  E. M. Pinches,et al.  The role of synchrony and oscillations in the motor output , 1999, Experimental Brain Research.

[58]  L. Ting,et al.  Functional muscle synergies constrain force production during postural tasks. , 2008, Journal of biomechanics.

[59]  Dick F. Stegeman,et al.  Reliability and Agreement of Intramuscular Coherence in Tibialis Anterior Muscle , 2014, PloS one.

[60]  C. D. De Luca,et al.  Myoelectric signal versus force relationship in different human muscles. , 1983, Journal of applied physiology: respiratory, environmental and exercise physiology.

[61]  D. Farina,et al.  Influence of common synaptic input to motor neurons on the neural drive to muscle in essential tremor. , 2015, Journal of neurophysiology.

[62]  G. Allison,et al.  The relationship between EMG median frequency and low frequency band amplitude changes at different levels of muscle capacity. , 2002, Clinical biomechanics.

[63]  D. Farina,et al.  The human motor neuron pools receive a dominant slow‐varying common synaptic input , 2016, The Journal of physiology.

[64]  Lena H Ting,et al.  A limited set of muscle synergies for force control during a postural task. , 2005, Journal of neurophysiology.

[65]  Dario Farina,et al.  A surface EMG generation model with multilayer cylindrical description of the volume conductor , 2004, IEEE Transactions on Biomedical Engineering.