Figure 1. Overview of the approach.

(A) During isometric contractions, electromyographic (EMG) signals can be considered as the sum of all action potentials that originate from the muscle fibres of all the active motor units that lie within the electrodes recording zone. The shape of the recorded action potentials differs across electrodes when recorded with an array of surface or intramuscular electrodes. The EMG signal and each individual MUAP profile depends on the position of the electrode, as highlighted by the different colours. (B) Decomposing EMG signals consists of solving the inverse problem, that is, to estimate the discharge times of the active motor units from the EMG signals. Our software uses a fast independent component analysis (fasICA) to optimise a set of separation vectors for each motor unit. To this end, each separation vector is iteratively optimised to maximise the sparseness of the motor unit pulse train. At the end of this step, the motor unit pulse train is refined, and a k-mean classification is applied to separate the high peaks, which represent the targeted motor unit spikes, from the low peaks (other motor units and noise). (C) During the online EMG decomposition, the extended EMG signals recorded over 125 ms segments are projected on the separation vectors, and the peaks are detected using the function ‘islocalmax’. Each peak is classified as spike or noise depending on the distance separating them from the centroids of the classes identified during the calibration. At the end of this process, the motor unit firing activity is translated into visual feedback, in the form of a raster plot, a quadrant, or the smoothed firing rate of an identified motor unit.

Figure 1—figure supplement 1. Workflow of a typical experimental session with I-Spin live.