The application of independent component analysis to the multi-channel surface electromyographic signals for separation of motor unit action potential trains: part II-modelling interpretation.

The purpose of this article was to investigate whether or not FastICA can separate identical motor unit action potential trains (MUAPTs) of the 8-channel surface electromyographic (sEMG) signals constructed by an sEMG model into the independent components. Firstly, we have examined how much the increase of motor units (MUs) in the simulated sEMG signals influenced the performance on the separation of MUAPTs by kurtosis. The decreased trend of mean kurtosis on both sEMG signals and their independent components were observed as MUs were increased. These data suggested that the separation performance decayed when MUs were increased. Secondary, the differences between the independent components and the principal components have been also applied to the simulated sEMG signals with or without time delay between the sEMG channels. FastICA could successfully separate identical MUAPTs with no time delay but principal component analysis (PCA) could not do so. Against it, both FastICA and PCA could not separate MUAPTs with some time delay. In conclusion, our results suggested that FastICA could separate identical MUAPTs with no time delay into the independent components by FastICA, which might offer a new technique for the separation of interfered MUAP waveforms based on statistical properties of sEMG signal distributions.