Now showing 1 - 3 of 3
  • Publication
    The Influence of Force Level and Motor Unit Coherence on Nonlinear Surface EMG Features Examined Using Model Simulation
    Nonlinear features extracted from surface EMG signals have been previously used to infer information on coherent or synchronous activity in the underlying motor unit discharges. However, it has not yet been assessed how these features are affected by the density of the surface EMG signal, and whether changes in the level of muscle activation can influence the effective detection of correlated motor unit firing. To examine this, a motoneuron pool model receiving a beta-band modulated cortical input was used to generate correlated motor unit firing trains. These firing trains were convolved with motor unit action potentials generated from an anatomically accurate electrophysiological model of the first dorsal interosseous muscle. The sample entropy (SampEn) and percentage determinism (%DET) of recurrence quantification analysis were calculated from the composite surface EMG signals, for signals comprised of both correlated and uncorrelated motor unit firing trains. The results show that although both SampEn and %DET are influenced by motor unit coherence, they are differentially affected by muscle activation and motor unit distribution. The results also suggest that sample entropy may provide a more accurate assessment of the underlying motor unit coherence than percentage determinism, as it is less sensitive to factors unrelated to motor unit synchrony.
      383Scopus© Citations 2
  • Publication
    Estimation of dispersive properties of encapsulation tissue surrounding deep brain stimulation electrodes in the rat
    The aim of this study was to estimate the electrical properties of the encapsulation tissue surrounding chronically implanted electrodes for deep brain stimulation in the rat. The impedance spectrum of a concentric bipolar microelectrode implanted in the rat brain was measured immediately following surgery and after 8 weeks of implantation. The experimental impedance data were used in combination with a finite element model of the rat brain using a parametric sweep method to estimate the electrical properties of the tissue surrounding the electrode in acute and chronic conditions. In the acute case, the conductivity and relative permittivity of the peri-electrode space were frequency independent with an estimated conductivity of 0.38 S/m and relative permittivity of 123. The electrical properties of the encapsulation tissue in the chronic condition were fitted to a dispersive Cole-Cole model. The estimated conductivity and relative permittivity in the chronic condition at 1 kHz were 0.028 S/m and 2×10 5 , respectively. The estimated tissue properties can be used in combination with computational modeling as a basis for optimization of chronically implanted electrodes to increase the efficacy of long-term neural recording and stimulation.
      555Scopus© Citations 1
  • Publication
    Anatomically accurate model of EMG during index finger flexion and abduction derived from diffusion tensor imaging
    This study presents a modelling framework in which information on muscle fiber direction and orientation during contraction is derived from diffusion tensor imaging (DTI) and incorporated in a computational model of the surface electromyographic (EMG) signal. The proposed model makes use of the principle of reciprocity to simultaneously calculate the electric potentials produced at the recording electrode by charges distributed along an arbitrary number of muscle fibers within the muscle, allowing for a computationally efficient evaluation of extracellular motor unit action potentials. The approach is applied to the complex architecture of the first dorsal interosseous (FDI) muscle of the hand to simulate EMG during index finger flexion and abduction. Using diffusion tensor imaging methods, the results show how muscle fiber orientation and curvature in this intrinsic hand muscle change during flexion and abduction. Incorporation of anatomically accurate muscle architecture and other hand tissue morphologies enables the model to capture variations in extracellular action potential waveform shape across the motor unit population and to predict experimentally observed differences in EMG signal features when switching from index finger abduction to flexion. The simulation results illustrate how structural and electrical properties of the tissues comprising the volume conductor, in combination with fiber direction and curvature, shape the detected action potentials. Using the model, the relative contribution of motor units of different sizes located throughout the muscle under both conditions is examined, yielding a prediction of the detection profile of the surface EMG electrode array over the muscle cross-section.
      349Scopus© Citations 21