This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition of experimental electrode array SEMG signals. A two-source method was performed by simultaneous concentric needle EMG and electrode array SEMG recordings from the human first dorsal interosseous (FDI) muscle, using a protocol commonly applied in clinical EMG examination. The electrode array SEMG was automatically decomposed by the APFP while the motor unit action potential (MUAP) trains were also independently identified from the concentric needle EMG. The degree of agreement of the common motor unit (MU) discharge timings decomposed from the two different categories of EMG signals was assessed. A total of 861 and 217 MUs were identified from the 114 trials of simultaneous high density SEMG and concentric needle EMG recordings, respectively. Among them 168 common (MUs) were found with a high average matching rate of [Formula: see text] for the discharge timings. The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.
Keywords: Surface EMG; automatic decomposition; progressive FastICA peel-off; two-source validation.