Motor unit tracking using blind source separation filters and waveform cross-correlations: reliability under physiological and pharmacological conditions

Abstract

Recent advancements in the analysis of high-density surface electromyography (HDsEMG) have enabled the identification, and tracking, of motor units (MUs) to study muscle activation. This study aimed to evaluate the reliability of MU tracking using two common methods: blind source separation filters and two-dimensional waveform cross-correlation. An experiment design was developed to assess physiological reliability, and reliability for a drug intervention known to reduce the firing rate of motoneurones (cyproheptadine). HDsEMG signals were recorded from tibialis anterior during isometric dorsiflexions to 10%, 30%, 50% and 70% of maximal voluntary contraction. MUs were matched within session (2 hr) using the filter method, and between sessions (7 days) via the waveform method. Both tracking methods demonstrated similar reliability during physiological conditions (e.g., MU discharge: filter ICC 10% of MVC = 0.76, to 70% of MVC = 0.86; waveform ICC: 10% of MVC = 0.78, to 70% of MVC = 0.91). Although reliability slightly reduced after the pharmacological intervention, there were no discernible differences in tracking performance (e.g., MU disc filter ICC: 10% of MVC = 0.73, to 70% of MVC = 0.75; DR waveform ICC: 10% of MVC = 0.84, to 70% of MVC = 0.85). The poorest reliability typically occurred at higher contraction intensities, which aligned with the greatest variability in MU characteristics. This study confirms that tracking method may not impact the interpretation of MU data, provided that an appropriate experiment design is employed. However, caution should be used when tracking MUs during higher intensity isometric contractions.

Antagonism of 5‐HT2 receptors attenuates self‐sustained firing of human motor units

“…MUs were tracked in the current study via the decomposition filter method, which has previously demonstrated high reliability during pharmacological interventions (Goodlich et al, 2023b). To investigate the effect of contraction type, MUs were tracked between the pre-drug 10% MVC trapezoid and pre-drug sombrero contractions.…”

Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking

“…Within each group, it was indeterminate whether one or multiple MUs were involved. In other studies involving the tracking of individual MUs, the MUAP waveform similarity was typically used and the performance of MUAP classification was only verified with isometric muscle contractions and fixed limb postures [6]- [16]- [20] where the MUAP waveforms were relatively stable. However, multiple factors, such as the MU firing rate [21]- [22], sustained muscle contractions [23] and electrode displacement [20], can induce the variation of MUAP profiles.…”

Adaptive EMG decomposition in dynamic conditions based on online learning metrics with tunable hyperparameters

“…While the cross-correlation coefficient is arguably a more common metric for MUAP tracking in isometric contractions [41][42][43], its use during dynamics should be treated with caution due to its insensitivity to amplitude changes. While Oliveira and Negro [15] pioneered its used to track MUAPs from the tibialis anterior between dynamic ankle dorsiflexions, their success may be explained by the limited range of motion explored (∼ 20 • ).…”