Higher-order statistics used for decomposition of SEMGs

The paper describes a higher-order statistics (HOS) approach feasible for decomposition of compound signals. Our novel method introduces asymptotically exact interpolation-based computation of bicepstra with no aliasing. We analysed it in order to establish the conditions under which surface electromyograms (SEMGs) could be decomposed onto their building components, i.e. motor-unit action potentials. The experiments with synthetic SEMGs showed that the results depend highly on the type of the action potentials (APs) respected. Using our novel bicepstral decomposition, the first-norm error for the dipole-based APs falls under 50% of the mean absolute value of the signal samples only with the signals of lengths of over 110000 samples and interpolation level of 4096. The developed decomposition approach be considered suitable for further HOS-based decomposition of SEMGs