Development of a computational tool for sEMG decomposition
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Motor unit decomposition from HD-sEMG (high density surface electromyography) provides valuable information for reviewing the neuromuscular system. The purpose of this study was to implement and validate the framework of the convolutional blind source separation proposed by Negro [1]. We tested on the Hyser (High densitY Surface Electromyogram) dataset with healthy subjects that perform low force finger movements during the execution of a task performed with a system of HD sEMG of 256 channels. Motor units were extracted using 3-second sliding windows with strict quality criteria (silhouette score ≥0.85). Decomposition yielded 4,536 quality motor units with a mean silhouette score of 0.926±0.023. The test-retest reliability indicated moderate stability of quality assessed parameters (ICC=0.52-0.59), whereas appropriately variable firing characteristics (ICC<0.10), confirming that the algorithm produced consistent outputs while still able to detect the temporal discharge variability.
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