Comparison of filtering methods for real-time extraction of the volitional EMG component in electrically stimulated muscles
Matthew J. Hambly, Ana Carolina C. de Sousa, Claudio Pizzolato
Abstract
Recorded electromyograms (EMG) of electrically stimulated muscles can contain both an exogenous-evoked potential (M-wave) and an endogenous, or volitional, component. This study evaluated the effectiveness of three filtering methods (i.e., high-pass, adaptive, and comb), commonly used in neurorehabilitation, in extracting the volitional component of simulated and experimental EMG during upper-limb tasks. Volitional EMG and M-wave were simulated through a physiological model of muscle recruitment, comprising of a motor neuron pool and associated muscle fibres, superimposed to a stimulation artefact. Experimental EMG data during different levels of volitional muscle contraction in isometric and dynamic tasks were recorded from five unimpaired individuals. Electrical stimulation artefact was removed with different techniques (i.e., none, removing samples, blanking, and interpolation) to assess filter performance across time and frequency domains, and information content (i.e., Kolmogorov-Smirnov D-value). The experimental results agreed with the simulations, wherein the adaptive filter outperformed the other filters when using no artefact removal or removing artefact samples from the signal, while for the blanking and interpolation artefact removal methods, the adaptive and comb filters outperformed the high-pass filter. The adaptive and comb filters best estimated volitional muscle activity in electrically stimulated muscles. Results from this study will enable the enhanced design of real-time neuroprosthesis control.