Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays

  • D. Farina
  • W. Muhammad
  • E. Fortunato
  • O. Meste
  • R. Merletti
  • H. Rix


This work addresses the problem of estimating the conduction velocity (CV) of single motor unit (MU) action potentials from surface EMG signals detected with linear electrode arrays during voluntary muscle contractions. In ideal conditions, that is without shape or scale changes of the propagating signals and with additive white Gaussian noise, the maximum likelihood (ML) is the optimum estimator of delay. Nevertheless, other methods with computational advantages can be proposed; among them, a modified version of the beamforming algorithm is presented and compared with the ML estimator. In real cases, the resolution in delay estimation in the time domain is limited because of the sampling process. Transformation to the frequency domain allows a continuous estimation. A fast, high-resolution implementation of the presented multichannel techniques in the frequency domain is proposed. This approach is affected by a negligible decrease in performance with respect to ideal interpolation. Application of the ML estimator, based on two-channel information, to ten firings of each of three MUs provides a CV estimate affected by a standard deviation of 0.5 ms−1; the modified beamforming and ML estimators based on five channels provide a CV standard deviation of less than 0.1 ms−1 and allow the detection of statistically significant differences between the CVs of the three MUs. CV can therefore be used for MU classification.


Electromyography Linear electrode arrays Beamforming Maximum likelihood estimation Motor unit action potentials Muscle fibre conduction velocity estimation 


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Copyright information

© IFMBE 2001

Authors and Affiliations

  • D. Farina
    • 1
    • 2
  • W. Muhammad
    • 3
  • E. Fortunato
    • 3
  • O. Meste
    • 3
  • R. Merletti
    • 1
  • H. Rix
    • 3
  1. 1.Department of ElectronicsPolitecnico di TorinoTorinoItaly
  2. 2.Department d'Automatique et Informatique AppliquéeEcole Centrale de NantesNantesFrance
  3. 3.Laboratoire I3SSophia AntipolisFrance

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