How are Muscle Synergies Affected by Electromyography Pre-Processing?

Abstract

Muscle synergies have been used for decades to explain a variety of motor behaviors, both in humans and animals and, more recently, to steer rehabilitation strategies. However, many sources of variability such as factorization algorithms, criteria for dimensionality reduction and data pre-processing constitute a major obstacle to the successful comparison of the results obtained by different research groups. Starting from the canonical EMG processing we determined how variations in filter cut-off frequencies and normalization methods, commonly found in literature, affect synergy weights and inter-subject similarity (ISS) using experimental data related to a 15-muscles upper-limb reaching task. Synergy weights were not significantly altered by either normalization (maximum voluntary contraction - MVC - or maximum amplitude of the signal - SELF) or band-pass filter ([20-500 Hz] or [50-500] Hz). Normalization did, however, alter the amount of variance explained by a set of synergies, which is a criterion often used for model order selection. Comparing different low-pass (LP) filters (0.5 Hz, 4 Hz, 10 Hz, 20 Hz cut-offs) we showed that increasing the low pass filter cut-off had the effect of decreasing the variance accounted for by a set number of synergies and affected individual muscle contributions. Extreme smoothing (i.e., LP cut-off 0.5 Hz) enhanced the contrast between active and inactive muscles but had an unpredictable effect on the ISS. The results presented here constitute a further step towards a thoughtful EMG pre-processing for the extraction of muscle synergies.

Muscle Synergies Extracted Using Principal Activations: Improvement of Robustness and Interpretability

“…However, there are also other elements of the processing chain giving raise to the muscle synergies extraction that must be considered. In general, prior literature has drawn considerable attention to the influence of EMG pre-processing on muscle synergy extraction [34]- [36]. In particular, the importance of standardization in envelope estimation [37] and amplitude normalization [38], [39] were thoroughly investigated.…”

Muscle Synergy Constraints Do Not Improve Estimates of Muscle Activity From Static Optimization During Gait for Unimpaired Children or Children With Cerebral Palsy

“…Alternate methods of scaling synergies experimentally, such as by a maximum voluntary contraction or the use of force-to-EMG measurements (Borzelli et al, 2013), require the collection and integration of additional data, significantly complicating the implementation. Although the choice of amplitude scaling prior to calculating synergies can impact the relative weights of muscles within a given synergy (Shuman et al, 2017), a recent investigation by Kieliba et al (2018) found nearly identical synergy structures between EMG data normalized by maximum voluntary contractions or peak activations in healthy adults. The consistency of these synergy structures suggests that the relative weights of muscles within a synergy scaled by experimental data may only have a small impact on our results.…”

Interpretable machine learning models for classifying low back pain status using functional physiological variables

“…Subsequently, the signals were rectified and low-pass-filtered with a zero-lag, fourth-order, Butterworth filter (5 Hz) [31]. The maximal EMG amplitude of each muscle per repetition was extracted and averaged within a set to create a normalizing factor [32]. EMG amplitude of each muscle per lifting set was divided by the normalizing factor.…”