Analysis of motor unit spike trains estimated from high-density surface electromyography is highly reliable across operators

Abstract

There is a growing interest in decomposing high-density surface electromyography (HDsEMG) into motor unit spike trains to improve knowledge on the neural control of muscle contraction. However, the reliability of decomposition approaches is sometimes questioned, especially because they require manual editing of the outputs. We aimed to assess the inter-operator reliability of the identification of motor unit spike trains. Eight operators with varying experience in HDsEMG decomposition were provided with the same data extracted using the convolutive kernel compensation method. They were asked to manually edit them following established procedures. Data included signals from three lower leg muscles and different contraction intensities. After manual analysis, 126 ± 5 motor units were retained (range across operators: 119-134). A total of 3380 rate of agreement values were calculated (28 pairwise comparisons x 11 contractions/muscles x 4-28 motor units). The median rate of agreement value was 99.6%. Inter-operator reliability was excellent for both mean discharge rate and time at recruitment (intraclass correlation coefficient > 0.99). These results show that when provided with the same decomposed data and the same basic instructions, operators converge toward almost identical results. Our data have been made available so that they can be used for training new operators.

A deep CNN framework for neural drive estimation from HD-EMG across contraction intensities and joint angles

“…6). One potential reason is that the human operator manually edited the spike trains to add false negatives and remove false positives [29]. The high frequency components in the estimated neural drive, however, might not be an issue for human-robot interfacing, since a low-pass filter is typically used in EMG-based controllers to remove undesired high frequency control signals so as to generate smooth and stable movement commands for wearable robots [34], [35].…”

“…All participants provided their written informed consent prior to participation in the study. Note that the experimental data were a subset of a previously published dataset [29].…”

“…With the BSS method and manual editing, we identified, across all contraction tasks and subjects in each scenario, 20±6 MUs (range 8-30) with 12269±3715 spikes in scenario 1, 29±5 MUs (range [23][24][25][26][27][28][29][30][31][32][33][34][35] with 16616±5462 in scenario 2, and 19±7 MUs (range 7-29) with 31754±9261 spikes in scenario 3. Table . I shows the detailed numbers of MUs and spikes per participant for each contraction task.…”

Estimation of the firing behaviour of a complete motoneuron pool by combining electromyography signal decomposition and realistic motoneuron modelling

“…The four sets of experimental data used in this study, named as reported in the first column of Table 1, provide the time-histories of recorded MN spike trains and whole muscle force trace F ( t ) (left panel in Figure 1), and were obtained from the studies (Hug, Avrillon et al, 2021; Hug, Del Vecchio et al, 2021) and (Del Vecchio et al, 2019; Del Vecchio et al, 2020), as open-source supplementary material and personal communication, respectively. In these studies, the HDEMG signals were recorded with a sampling rate f s = 2048 Hz from the Tibialis Anterior (TA) and Gastrocnemius Medialis (GM) human muscles during trapezoidal isometric contractions.…”

Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals

“…The nature of this manual cleaning generally relates to the mixing system of interest, for example intracortical and intramuscular EMG (iEMG) decompositions generally require post-hoc examination of classes due to extensive class-dependent label noise [8] [19]. On the other hand, surface EMG (sEMG) decompositions also contain a degree of feature-dependent noise and so require further inspection of specific activations [20]. Whilst accurate, manual post-hoc "cleaning" is an extremely time-consuming process and in some cases not feasible because of the size of the datasets being source-separated [21].…”