Real time identification of active regions in muscles from high density surface electromyogram

Abstract

An innovative algorithm is proposed for the non-invasive localization of the active regions of a muscle. It is real time and opens potential future applications for prosthesis control and biofeedback.

Nonlinear spatio-temporal filter to reduce crosstalk in bipolar electromyogram

“…Due to the large detection volume, a surface EMG channel placed over a target muscle of interest records also an undesired crosstalk signal produced by the contraction of adjacent muscles [8,9,35,47]. Different advanced methods have been proposed to attenuate crosstalk [20,26,28,30,37,47], but using simple spatial filters is still the most used approach in many fields [17,19].…”

“…Advanced strategies have also been explored, e.g. decomposition algorithms [28,29] and inverse methods [26,30]. However, these processing algorithms are complicated and require a highdensity detection.…”

Optimal spatio-temporal filter for the reduction of crosstalk in surface electromyogram

“…2. using sophisticated inverse methods (estimating the location of active MUs) applied to data recorded with high-density systems, crosstalk can be possibly estimated and in part removed (potential application suggested on the basis of simulations, but still to be tested on experiments [20]).…”

“…1. under the hypotheses that the recorded data are linear instantaneous mixtures of independent signals produced by the investigated muscles (sources) and that the number of detected signals is larger than that of the muscles, an advanced blind source separation technique was applied to time frequency representations of the data to remove crosstalk [14]; however, the algorithm cannot be easily integrated in real time applications, as the mixing matrix changes in time and should be updated processing the data which are acquired; moreover, the assumptions limit the applications to small, superficial muscles which are close to each other (to reduce the effect of the volume conductor, mostly neglected due to the assumption of linear instantaneous mixture) which are not synergic (as a possible common drive would violate the assumption of independence of the sources); 2. using sophisticated inverse methods (estimating the location of active MUs) applied to data recorded with high-density systems, crosstalk can be possibly estimated and in part removed (potential application suggested on the basis of simulations, but still to be tested on experiments [20]).…”

Numerical Identification of Motor Units using an Optimal Control Approach

“…Kleine et al (2007)), a reliable and accurate method to determine where the motor units are located and where the trajectory of the muscle fibers run from the sEMG signal is not yet available. Previous works consider spatial data only [van den Doel et al (2008[van den Doel et al ( , 2011Liu et al (2015)] or use simple parametric models within a least squares approach [Mesin (2015)]. …”