Surface electromyogram signal modelling

Abstract

The paper reviews the fundamental components of stochastic and motor-unit-based models of the surface electromyogram (SEMG). Stochastic models used in ergonomics and kinesiology consider the SEMG to be a stochastic process whose amplitude is related to the level of muscle activation and whose power spectral density reflects muscle conduction velocity. Motor-unit-based models for describing the spatio-temporal distribution of individual motor-unit action potentials throughout the limb are quite robust, making it possible to extract precise information about motor-unit architecture from SEMG signals recorded by multi-electrode arrays. Motor-unit-based models have not yet been proven as successful, however, for extracting information about recruitment and firing rates throughout the full range of contraction. The relationship between SEMG and force during natural dynamic movements is much too complex to model in terms of single motor units.

Evaluation of the EMG–force relationship of trunk muscles during whole body tilt

“…Therefore, the resulting SEMG signal has a stochastic characteristic, also called ''noise-like interference pattern'' (McGill, 2004). It is correlated with global SEMG parameters like root mean square (rms) and median frequency (Bonato, 2001) to name the most common ones.…”

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

“…The first model is based on the energy modulation of Gaussian noise, and can be used to analyze the global information of sEMG signals using the amplitude and power spectrum (PS). The second model is based on a physiological property, and can precisely describe MU anatomy [20]. In this paper, we used the second model.…”

Piper rhythm of the electromyograms of the abductor pollicis brevis muscle during isometric contractions

“…Rhythmic oscillations of muscles at frequencies of 35-60 Hz were already noted in the electromyograms by Piper (1907). Stochastic models used in ergonomics and kinesiology consider the surface EMG to be generated by a stochastic process whose amplitude is related to the level of muscle activation and whose power spectral density reflects muscle fiber conduction velocity (McGill, 2004). Other models represent realizations of zero mean, nonstationary, mutually uncorrelated, random processes (Farina et al, 2008).…”