Unlocking the full potential of high-density surface EMG: novel non-invasive high-yield motor unit decomposition.

The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of movement, providing insights into motor neuron recruitment and discharge behaviour. However, current algorithms, while effective under certain conditions, face significant challenges in complex scenarios, as their accuracy and motor unit yield are highly dependent on anatomical differences among individuals. To address this issue, we recently introduced Swarm-Contrastive Decomposition (SCD), which dynamically adjusts the contrast function based on the distribution of the data. Here, we demonstrate the ability of SCD in identifying low-amplitude motor unit action potentials and effectively handling complex decomposition scenarios. We validated SCD using simulated and experimental HD-sEMG recordings and compared it with current state-of-the-art decomposition methods under varying conditions, including different excitation levels, noise intensities, force profiles, sexes and muscle groups. The proposed method consistently outperformed existing techniques in both the quantity of decoded motor units and the precision of their firing time identification. Across different simulated excitation levels, SCD detected, on average, 25.9 ±5.8 motor units vs. 13.9 ± 2.7 found by a state-of-the-art baseline approach. Across noise levels, SCD detected 19.8 ± 13.5 motor units, compared to 11.9 ± 6.9 by the baseline method. In simulated conditions of high synchronisation levels, SCD detected approximately three times as many motor units compared to previous methods (31.2 ± 4.3 for SCD, 10.5 ± 1.7 for baseline), while also significantly improving accuracy. These advancements represent a step forward in non-invasive EMG technology for studying motor unit activity in complex scenarios. KEY POINTS: High-density surface electromyography (HD-sEMG) decomposition provides information on how the nervous system controls muscles, but current methods struggle in complex conditions. Swarm-Contrastive Decomposition (SCD) is a new approach that dynamically adjusts how signals are separated, improving accuracy and increasing the sample of detected motor units. SCD successfully identifies more motor units, including those with low-amplitude signals, and performs well even in challenging conditions such as high-interference signals. In simulated ballistic contractions, SCD detected three times more motor units than previous methods while improving accuracy. These advancements could improve non-invasive studies of muscle function in movement, fatigue and neurological disorders.