A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Multichannel signal separation: methods and analysis
IEEE Transactions on Signal Processing
Frequency domain blind MIMO system identification based on second and higher order statistics
IEEE Transactions on Signal Processing
Computer Methods and Programs in Biomedicine
Muscle activity detection in electromyograms recorded during periodic movements
Computers in Biology and Medicine
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We are addressing a possible approach to the decomposition of surface electromyograms (SEMGs). It is based on higher-order cumulants implemented in a two-step procedure. Firstly, a multivariate version of the w-slice method is applied in order to extract coarse approximations of motor-unit action potentials (MUAPs) out of the measured SEMGs. Secondly, these coarse estimates are refined by modified Newton-Gauss iteration to achieve an optimum fit of the model-based and the observation-based cumulant estimates. All the necessary conditions are derived theoretically and, afterwards, implemented in simulation runs in order to prove the decomposition power of the proposed approach on synthetic SEMGs. The first-norm difference between the original and the decomposed MUAPs, obtained at the signal length of 102400 samples and expressed in percentage of the MUAP amplitude span, yields 5.4% in the noise-free case, 6.0% with a signal-to-noise ratio (SNR) of 10dB, and 6.5% with a SNR of 0 dB.