Inverse modelling to reduce crosstalk in high density surface electromyogram

Surface electromyogram (EMG) has a relatively large detection volume, so that it could include contributions both from the target muscle of interest and from nearby regions (i.e., crosstalk). This interference can prevent a correct interpretation of the activity of the target muscle, limiting the use of surface EMG in many fields. To counteract the problem, selective spatial filters have been proposed, but they reduce the representativeness of the data from the target muscle. A better solution would be to discard only crosstalk from the signal recorded in monopolar configuration (thus, keeping most information on the target muscle). An inverse modelling approach is here proposed to estimate the contributions of different muscles, in order to focus on the one of interest. The method is tested with simulated monopolar EMGs from superficial nearby muscles contracted at different force levels (either including or not model perturbations and noise), showing statistically significant improvements in information extraction from the data. The median over the entire dataset of the mean squared error in representing the EMG of the muscle under the detection electrode was reduced from 11.2% to 4.4% of the signal energy (5.3% if noisy data were processed); the median bias in conduction velocity estimation (from 3 monopolar channels aligned to the muscle fibres) was decreased from 2.12 to 0.72 m/s (1.1 m/s if noisy data were processed); the median absolute error in the estimation of median frequency was reduced from 1.02 to 0.67 Hz in noise free conditions and from 1.52 to 1.45 Hz considering noisy data.