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Non-invasive Decoding of the Motoneurons: A Guided Source Separation Method Based on Convolution Kernel Compensation

Mohammad Reza Mohebian1, Hamid Reza Marateb1, Saeed Karimimehr1,2

  • 1The Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran.

Frontiers in Computational Neuroscience
|April 20, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid surface electromyography (sEMG) decomposition method for identifying motor unit (MU) firing patterns. The guided source separation (GSS) approach identifies more MUs than existing methods, offering a promising tool for clinical neurophysiology.

Keywords:
electromyographykalman filtermotor unit identificationneural decodingsource separation

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Cortico-muscular coupling studies often rely on electromyographic (EMG) signal analysis, which can be non-trivial.
  • Direct neural decoding by decomposing EMG signals offers an alternative to amplitude estimation for assessing motor neuron pool behavior.

Purpose of the Study:

  • To propose a novel, fully-deterministic hybrid surface EMG (sEMG) decomposition approach for identifying motor unit (MU) firing patterns.
  • To combine template-based and Blind Source Separation (BSS) decomposition methods, termed guided source separation (GSS), for improved MU identification.

Main Methods:

  • Utilized a density-based clustering algorithm for initial MU identification and a modified gradient Convolution Kernel Compensation (gCKC) algorithm.
  • Incorporated a Kalman filter to reduce noise and enhance the convergence rate of MU filter identification.
  • Developed an adaptive soft-thresholding method for precise MU firing time estimation from spike trains.

Main Results:

  • The proposed GSS method identified an average of 16.41 ± 4.18 MUs with a Rate-of-Agreement (RoA) of 84.00 ± 0.06%.
  • Compared to the standard gCKC algorithm, which identified 12.10 ± 2.32 MUs with 90.78 ± 0.08% RoA, the GSS identified more MUs with comparable performance.
  • Algorithm performance correlated with signal quality but not with signal complexity across different force levels.

Conclusions:

  • The proposed hybrid GSS method is a promising offline tool for clinical neurophysiology, offering improved motor unit identification.
  • The method demonstrates potential for more comprehensive analysis of neural codes in cortico-muscular coupling studies.
  • Further validation in clinical settings is warranted to fully assess its utility.