Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

J Neural Eng. 2014 Feb;11(1):016008. doi: 10.1088/1741-2560/11/1/016008.


Objective: A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique.

Approach: The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction.

Main results: In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units.

Significance: The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Data Interpretation, Statistical
  • Electromyography / instrumentation*
  • Electromyography / methods*
  • Humans
  • Isometric Contraction / physiology
  • Linear Models
  • Motor Neurons / physiology*
  • Muscle Contraction / physiology
  • Muscle Fibers, Skeletal / physiology*
  • Muscle, Skeletal / physiology
  • Reproducibility of Results
  • Signal Processing, Computer-Assisted
  • Signal-To-Noise Ratio