Recordings of electrical activity from muscles allow us to identify the activity of pools of spinal motor neurons that send the neural drive for muscle activation. Decoding motor unit (MU) and motor neuron activity from muscle recordings can be performed by high-density (HD) electrode systems, both noninvasively (surface, HD-sEMG) and invasively (intramuscular, HD-iEMG). HD-sEMG recordings are obtained by grids placed on the skin surface while HD-iEMG signals can be acquired by microarrays of electrodes. While it has been shown that HD-iEMG allows the accurate decoding of a larger number of motor units when compared to HD-sEMG, the dependence of motor unit yield on the parameters of the micro-arrays is still unexplored. Here, we used recently developed HD-iEMG electrodes to record from hundreds of recording sites within the muscle. This allowed us to investigate the impact of electrode number, inter-electrode distance, and the number of muscle insertions on the ability to sample motor units within the muscle. Specifically, we recorded both HD-sEMG and HD-iEMG from the Tibialis Anterior muscle of two healthy subjects at various contraction intensities (10%, 30%, and 70% of maximum voluntary contraction, MVC). For the first time, we present intramuscular recordings with more than 140 electrodes inside a single muscle, achieved through multiple implants of highdensity micro-arrays. Through systematic offline analyses of these recordings, we tested different electrode configurations to identify optimal setups for accurately capturing motor unit activity. The results revealed that the density of electrodes in the micro-arrays is the most critical factor for maximising the number of identified motor units and ensuring very high accuracy. Comparisons between intramuscular and surface recordings also confirmed that HD-iEMG consistently captures larger and more stable numbers of motor units across subjects and contraction levels. These results underscore the potential of HD-iEMG as a powerful tool for both clinical and research settings, particularly when precise motor unit decomposition is crucial.

The decomposition of neurophysiological recordings into their constituent neural sources is of major importance to a diverse range of neuroscientific fields and neuroengineering applications. The advent of high density electrode probes and arrays has driven a major need for novel semi-automated and automated blind source separation methodologies that take advantage of the increased spatial resolution and coverage these new devices offer. Independent component analysis (ICA) offers a principled theoretical framework for such algorithms, but implementation inefficiencies often drive poor performance in practice, particularly for sparse sources. Here we observe that the use of a single non-linear optimization function to identify spiking sources with ICA often has a detrimental effect that precludes the recovery and correct separation of all spiking sources in the signal. We go on to propose a projection-pursuit ICA algorithm designed specifically for spiking sources, which uses a particle swarm methodology to adaptively traverse a polynomial family of non-linearities approximating the asymmetric cumulants of the sources. We robustly prove state-of-the-art decomposition performance on recordings from high density intramuscular probes and demonstrate how the particle swarm quickly finds optimal contrast non-linearities across a range of neurophysiological datasets.