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.

No abstract

Real-time intramuscular electromyography (iEMG) decomposition, which is largely required in the neurological studies and applications, is a complex procedure that involves identifying the motor neuron spike trains from a streaming iEMG recording. We have previously proposed a sequential decomposition algorithm based on a Hidden Markov Model of EMG, that used Bayesian filter to estimate unknown parameters of motor units (MUs) spike trains, as well as their action potentials (MUAPs). In this paper we present a parallel computation implementation of this algorithm on Graphics Processing Unit (GPU), as well as a number of modifications applied to the original model in order to achieve a real-time performance of the algorithm. Specifically, the Kalman filter, previously used to estimate the MUAPs, is replaced by a least-mean-square filter. Additionally, we introduce a number of heuristics that help to omit the most improbable decomposition scenarios while searching for the best solution. Then, a GPU-implementation of the proposed algorithm is presented. Dozens of simulated iEMG signals containing up to 10 active MUs, as well as five experimental fine-wire iEMG signals acquired from tibialis anterior, were decomposed in real time. The accuracy of decompositions depended on the level of muscle activation, but in all cases exceeded 85%.