Decomposition of surface EMG signals using non-linear LMS optimisation of higher-order cumulants

Abstract

Deals with the problem of decomposition of surface electromyograms (SEMG). According to the physiological facts, a multiple-input multiple-output (MIMO) is used. The measured signals are taken as the channel responses corresponding to the motor-unit action potentials (MUAPs) convolution by the innervation pulse trains. The decomposition is based on the third-order cumulants whose values enter as coefficients of nonlinear system of equations. The system is solved by nonlinear least mean square (LMS) optimisation. Synthetic SEMG signals from a MIMO(2,3) with additive Gaussian noise with SNRs of 10 and 0 dB prove that a successful multichannel decomposition is possible also in very noisy environments.