Inverse modelling to reduce crosstalk in high density surface electromyogram

Evaluating the Relationship Between Surface and Intramuscular-Based Electromyography Signals: Implications of Subcutaneous Fat Thickness

Intramuscular (iEMG) and surface electromyographic (sEMG) signals have been compared previously using predictive regression equations, finite element modeling, and correlation and cross-correlation analyses. Although subcutaneous fat thickness (SCFT) has been identified as a primary source of sEMG signal amplitude attenuation and low-pass filter equivalence, few studies have explored the potential effect of SCFT on sEMG and iEMG signal characteristics. The purpose of this study was to investigate the relationship between normalized submaximal iEMG and sEMG signal amplitudes collected from 4 muscles (rectus femoris, vastus lateralis, infraspinatus, and erector spinae) and determine whether SCFT explains more variance in this relationship. The effect of sex was also explored. Linear regression models demonstrated that the relationship between sEMG and iEMG was highly variable across the muscles examined (adjusted coefficient of determination [Adj R2] = .02-.74). SCFT improved the model fit for vastus lateralis, although this relationship only emerged with the inclusion of sex as a covariate. Thus, this research suggests that SCFT is not a prominent factor affecting the linearity between sEMG and iEMG. Researchers should investigate other parameters that may affect the linearity between sEMG and iEMG signals.

Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram

Objective.The wide detection volume of surface electromyogram (EMG) makes it prone to crosstalk, i.e. the signal from other muscles than the target one. Removing this perturbation from bipolar recordings is an important open problem for many applications.Approach.An innovative nonlinear spatio-temporal filter is developed to estimate the EMG generated by the target muscle by processing noisy signals from two bipolar channels, placed over the target and the crosstalk muscle, respectively. The filter is trained on some calibration data and then can be applied on new signals. Tests are provided in simulations (considering different thicknesses of the subcutaneous tissue, inter-electrode distances, locations of the EMG channels, force levels) and experiments (from pronator teres and flexor carpi radialis of 8 healthy subjects).Main results.The proposed filter allows to reduce the effect of crosstalk in all investigated conditions, with a statistically significant reduction of its root mean squared of about 20%, both in simulated and experimental data. Its performances are also superior to those of a blind source separation method applied to the same data.Significance.The proposed filter is simple to be applied and feasible in applications in which single bipolar channels are placed over the muscles of interest. It can be useful in many fields, such as in gait analysis, tests of myoelectric fatigue, rehabilitation with EMG biofeedback, clinical studies, prosthesis control.