Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition

Abstract

Decomposition of electromyograms (EMG) is a key approach to investigating motor unit plasticity. Various signal processing techniques have been developed for high density surface EMG decomposition, among which the convolution kernel compensation (CKC) has achieved high decomposition yield with extensive validation. Very recently, a progressive FastICA peel-off (PFP) framework has also been developed for high density surface EMG decomposition. In this study, the CKC and PFP methods were independently applied to decompose the same sets of high density surface EMG signals. Across 91 trials of 64-channel surface EMG signals recorded from the first dorsal interosseous (FDI) muscle of 9 neurologically intact subjects, there were a total of 1477 motor units identified from the two methods, including 969 common motor units. On average,10.6±4.3common motor units were identified from each trial, which showed a very high matching rate of97.85±1.85% in their discharge instants. The high degree of agreement of common motor units from the CKC and the PFP processing provides supportive evidence of the decomposition accuracy for both methods. The different motor units obtained from each method also suggest that combination of the two methods may have the potential to further increase the decomposition yield.