| Popis: |
The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified using grids of electromyographic (EMG) electrodes and source separation methods, i.e., EMG decomposition. In this study, we combined computational and experimental approaches to assess how the design parameters of grids of electrodes influence the number and characteristics of the motor units identified. We first computed the percentage of unique motor unit action potentials that could be theoretically discriminated in a pool of 200 simulated motor units when recorded with grids of various sizes and interelectrode distances (IED). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). Increasing both the density and the number of electrodes, as well as the size of the grids, increased the number of motor units that the EMG decomposition could theoretically discriminate, i.e., up to 82.5% of the simulated pool (range: 30.5-82.5%). Experimentally, the configuration with the largest number of electrodes and the shortest IED maximized the number of motor units identified (56 ± 14; range: 39-79) and the percentage of low-threshold motor units identified (29 ± 14%). Finally, we showed with a prototyped grid of 400 electrodes (IED: 2 mm) that the number of identified motor units plateaus beyond an IED of 2-4 mm. These results showed that larger and denser surface grids of electrodes help to identify a larger and more representative pool of motor units than currently reported in experimental studies.Significance statementIndividual motor unit activities can be exactly identified by blind-source separation methods applied to multi-channel EMG signals recorded by grids of electrodes. The design parameters of grids of EMG electrodes have never been discussed and are usually arbitrarily fixed, often based on commercial availability. In this study, we showed that using larger and denser grids of electrodes than conventionally applied can drastically increase the number of motor units identified. These samples of motor units are moreover more balanced between high- and low-threshold motor units and provide a more representative sampling of neural drive to muscles. Gathering large datasets of motor units using large and dense grids will impact the study of motor control, neuromuscular modelling, and human-machine interfacing. |
