Nikolay S. Stoykov

A multiple-layer finite-element model of the surface EMG signal

The effect of skin, muscle, fat, and bone tissue on simulated surface electromyographic (EMG) signals was examined using a finite-element model. The amplitude and frequency content of the surface potential were observed to increase when the outer layer of a homogeneous muscle model was replaced with highly resistive skin or fat tissue. The rate at which the surface potential decreased as the fiber was moved deeper within the muscle also increased. Similarly, the rate at which the surface potential decayed around the surface of the model, for a constant fiber depth, increased. When layers of subcutaneous fat of increasing thickness were then added to the model, EMG amplitude, frequency content, and the rate of decay of the surface EMG signal around the limb decreased, due to the increased distance between the electrodes and the active fiber. The influence of bone on the surface potential was observed to vary considerably, depending on its location. When located close to the surface of the volume conductor, the surface EMG signal between the bone and the source and directly over the bone increased, accompanied by a slight decrease on the side of the bone distal to the active fiber. The results emphasize the importance of distinguishing between the effects of material properties and the distance between source and electrode when considering the influence of subcutaneous tissue, and suggest possible distortions in the surface EMG signal in regions where a bone is located close to the skin surface.

The effect of subcutaneous fat on myoelectric signal amplitude and cross-talk.

The effect of subcutaneous fat on myoelectric signal amplitude and cross-talk was studied using finite element (FE) models of electromyogram (EMG) signal propagation. A FE model of the upper arm consisted of skin, fat, muscle and bone tissues in concentric layers. Single muscle fibre action potentials were simulated for muscle fibres at a variety of depths and combined to simulate surface EMG interference patterns. As fat layers of 3, 9 and 18mm were added to the model, the RMS (root mean square) amplitude of the surface EMG signal directly above the centre of the active muscle decreased by 31.3, 80.2 and 90.0%, respectively. Similarly, surface EMG cross-talk above the region of inactive muscle increased as the fat layer thickness increased. The surface EMG RMS amplitude fell below 5% of its value above the centre of the muscle at 14°, 17°, 34° and 47° from the edge of the active muscle with fat layers of 0, 3, 9 and 18mm, respectively. An additional model was developed with the subcutaneous fat layer thinned from 9mm to 3mm in a small, focal region under a pair of recording electrodes. Reducing the fat layer in this manner caused the surface EMG amplitude at the electrodes to increase by 241% and decreased the EMG cross-talk by 68%; this was near the values for the 3mm uniform fat layer. This demonstrates that fat reduction surgery can increase surface EMG signal amplitude and signal independence for improved prosthesis control.

Independence of myoelectric control signals examined using a surface EMG model

The detection volume of the surface electromyographic (EMG) signal was explored using a finite-element model, to examine the feasibility of obtaining independent myoelectric control signals from regions of reinnervated muscle. The selectivity of the surface EMG signal was observed to decrease with increasing subcutaneous fat thickness. The results confirm that reducing the interelectrode distance or using double-differential electrodes can increase surface EMG selectivity in an inhomogeneous volume conductor. More focal control signals can be obtained, at the expense of increased variability, by using the mean square value, rather than the root mean square or average rectified value.

Volume conduction in an anatomically based surface EMG model

A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subjects upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation.

Frequency- and time-domain FEM models of EMG: capacitive effects and aspects of dispersion

Electromyography (EMG) simulations have traditionally been based on purely resistive models, in which capacitive effects are assumed to be negligible. Recent experimental studies suggest these assumptions may not be valid for muscle tissue. Furthermore, both muscle conductivity and permittivity are frequency-dependent (dispersive). In this paper, frequency-domain and time-domain finite-element models are used to examine the impact of capacitive effects and dispersion on the surface potential of a volume conductor. The results indicate that the effect of muscle capacitance and dispersion varies dramatically. Choosing low conductivity and high permittivity values in the range of experimentally reported data for muscle can cause displacement currents that are larger than conduction currents with corresponding reduction in surface potential of up to 50% at 100 Hz. Conductivity and permittivity values lying toward the middle of the reported range yield results which do not differ notably from purely resistive models. Also, excluding dispersion can also cause large error-up to 75% in the high frequency range of the EMG. It is clear that there is a need to establish accurate values of both conductivity and permittivity for human muscle tissue in vivo in order to quantify the influence of capacitance and dispersion on the ENIG signal.

Finite element modeling of electromagnetic signal propagation in a phantom arm

Improving the control of artificial arms remains a considerable challenge. It may be possible to graft remaining peripheral nerves in an amputated limb to spare muscles in or near the residual limb and use these nerve-muscle grafts as additional myoelectric control signals. This would allow simultaneous control of multiple degrees of freedom (DOF) and could greatly improve the control of artificial limbs. For this technique to be successful, the electromyography (EMG) signals from the nerve-muscle grafts would need to be independent of each other with minimal crosstalk. To study EMG signal propagation and quantify crosstalk, finite element (FE) models were developed in a phantom-arm model. The models were validated with experimental data collected by applying sinusoidal excitations to a phantom-arm model and recording the surface electric potential distribution. There was a very high correlation (r>0.99) between the FEM data and the experimental data, with the error in signal magnitude generally less than 5%. Simulations were then performed using muscle dielectric properties with static, complex, and full electromagnetic solvers. The results indicate that significant displacement currents can develop (>50% of total current) and that the fall-off of surface signal power varies with how the signal source is modeled.

Finite-element time-domain algorithms for modeling linear Debye and Lorentz dielectric dispersions at low frequencies

We present what we believe to be the first algorithms that use a simple scalar-potential formulation to model linear Debye and Lorentz dielectric dispersions at low frequencies in the context of finite-element time-domain (FETD) numerical solutions of electric potential. The new algorithms, which permit treatment of multiple-pole dielectric relaxations, are based on the auxiliary differential equation method and are unconditionally stable. We validate the algorithms by comparison with the results of a previously reported method based on the Fourier transform. The new algorithms should be useful in calculating the transient response of biological materials subject to impulsive excitation. Potential applications include FETD modeling of electromyography, functional electrical stimulation, defibrillation, and effects of lightning and impulsive electric shock.

Recording intramuscular EMG signals using surface electrodes

The control of multifunctional myoelectric prostheses is a substantive area of research with the potential to dramatically improve the independence of transradial amputees. We present preliminary data for the development of a new technique for obtaining multiple electromyographic (EMG) signals for controlling multifunctional myoelectric hand and wrist prostheses. A completely embedded passive conductor is proposed to transmit intramuscular EMG signals to a distant location just beneath the skin surface with a subcutaneous terminal. These signals can then be recorded with conventional surface electrodes. The surface recorded intramuscular EMG (SRI EMG) signals would closely follow the electrical potential at the muscle fiber source. They would be extremely selective, and the well-known effect of spatial filtering, which reduces the amplitude and frequency content of surface EMG signals, would be virtually eliminated. It would, therefore, be possible to access control signals from deep or small muscles that would otherwise be unavailable. Based on this technique, a new generation of multifunction myoelectric prostheses can be developed. The technique is a simple, inexpensive, and robust alternative to implanted telemetry systems and percutaneous electrodes.

A multi-layer finite element model of the surface EMG signal

The influence of skin, adipose tissue and bone on the rate of decay of the surface EMG signal around the limb was explored using a new finite element model. Replacing the outer layer of a homogeneous muscle model with a layer of highly resistive tissue, such as skin or fat, results in an increase In the surface potential. This also causes an increase in the rate of decay of EMG amplitude with increasing source depth and with increasing angular displacement from the source. EMG signals are examined as a bone is positioned at different locations throughout the muscle. Depending on its location, the highly resistive bone can significantly affect the amplitude of the surface potential. In a model of the upper arm, cross-talk around the limb was examined as subcutaneous tissue thickness was varied. EMG cross-talk was observed to increase with subcutaneous fat thickness. This is due to the relative increase in distance between source and recording site, rather than the material properties of the adipose tissue. The results illustrate the importance of including multiple tissue layers and inhomogeneities such as bone, when exploring aspects of surface EMG amplitude such as cross-talk.

A finite-element analysis of the effect of muscle insulation and shielding on the surface EMG signal

We simulate the effect that insulating or shielding a muscle may have on electromyographic signal propagation using the finite element method. The results suggest that the crosstalk between insulated or shielded muscles is small but that it increases with increasing subcutaneous fat. The findings may be useful in the control of multifunctional prostheses.