Effects of Spatial and Signal-Imposed Noises on Motor Unit Decomposition

High-density surface electromyography (HD-sEMG) decomposition offers insights into the neural drive through observation of individual motor units (MUs). However, ensuring that this method remains reliable under real-world signal degradations is crucial for its broader application. Therefore, we investigated the impact of three commonly modeled signal degradations on convolutive blind-source-separation (BSS) MU decomposition. 192 HD-sEMG channels were recorded from the forearm muscles of thirteen healthy participants during six wrist movements. Three broad categories of perturbation were introduced, including additive white Gaussian noise (WGN), channel loss, and electrode shift. These perturbations were chosen to mimic challenges encountered in practice, such as ambient electrical noise, electrode failures, and sensor displacement in order to test the MU decomposition algorithms sensitivity. Then, the effects of perturbations on the quantity and quality of extracted MUs and neural drive estimation were assessed. Under non-perturbed conditions, an average of 179 ± 40 MUs were extracted. Severe global WGN significantly reduced extracted MUs by approximately 81%. In contrast, more localized WGN, or channel loss as high as 15%, and electrode shift had minimal impact, with reductions in the number of MU decomposed being less than 6%. Reconstruction of neural drive through smoothed cumulative spike trains was significantly impaired by global WGN, thus leading to increased root mean square error when compared to conditions, while localized perturbations had negligible effects. Therefore, the BSS-based MU decomposition methods seem to be robust against localized noise, channel loss, and minor electrode shifts but are vulnerable to global additive noise. These results highlight the importance of carefully applying MU decomposition approaches in practical settings, maintaining high SNRs in EMG recordings, and preprocessing noise treatment to specific MU-decomposition needs.