In recent years, there has been significant research and development in the area of advanced circuits and systems for the recording of the electroneurogram (ENG) from peripheral nerve signals. This thesis presents an emulator of peripheral nerve for the testing of bio-potential recording systems under development reducing the need for early in vitro experiments and providing reproducible results. The emulator can be configured as an artificial nerve for ENG recording, which emulates the natural behavior of a nerve and provides an interface to the circuit under test. It is representative of a real nerve in terms of impedances, electrode voltages and action potential propagation characteristics as seen when recording from a nerve cuff electrode. Its dynamic behavior is controlled by a series of linked microcontrollers. The emulator provides different user selectable scenarios including single fiber action potential (SFAP), compound action potential propagation following stimulation (CAP), naturally occurring nerve traffic, and additional interference. This emulator circuit is designed using MATLAB and Cadence Spectre to perform circuit simulation. Measured results of the emulator based on a PCB including microcontrollers (PIC series, Microchip) are reported.

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A finite element (FE) model for the generation of single fiber action potentials (SFAPs) in a muscle undergoing various degrees of fiber shortening has been developed. The muscle is assumed to be fusiform with muscle fibers following a curvilinear path described by a Gaussian function. Different degrees of fiber shortening are simulated by changing the parameters of the fiber path and maintaining the volume of the muscle constant. The conductivity tensor is adapted to the muscle fiber orientation. At each point of the volume conductor, the conductivity of the muscle tissue in the direction of the fiber is larger than that in the transversal direction. Thus, the conductivity tensor changes point-by-point with fiber shortening, adapting to the fiber paths. An analytical derivation of the conductivity tensor is provided. The volume conductor is then studied with an FE approach using the analytically derived conductivity tensor (Mesin, Joubert, Hanekom, Merletti&Farina 2006). Representative simulations of SFAPs with the muscle at different degrees of shortening are presented. It is shown that the geometrical changes in the muscle, which imply changes in the conductivity tensor, determine important variations in action potential shape, thus affecting its amplitude and frequency content. The model is expanded to include the simulation of motor unit action potentials (MUAPs). Expanding the model was done by assigning each single fiber (SF) in the motor unit (MU) a random starting position chosen from a normal distribution. For the model 300 SFs are included in an MU, with an innervation zone spread of 12 mm. Only spatial distribution was implemented. Conduction velocity (CV) was the same for all fibers of the MU. Representative simulations for the MUAPs with the muscle at different degrees of shortening are presented. The influence of interelectrode distance and angular displacement are also investigated as well as the influence of the inclusion of the conductivity tensor. It has been found that the interpretation of surface electromyography during movement or joint angle change is complicated owing to geometrical artefacts i.e. the shift of the electrodes relative to the muscle fibers and also because of the changes in the conductive properties of the tissue separating the electrode from the muscle fibers. Detection systems and electrode placement should be chosen with care. The model provides a new tool for interpreting surface electromyography (sEMG) signal features with changes in muscle geometry, as happens during dynamic contractions. / Dissertation (MEng (Bio-Engineering))--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / MEng (Bio-Engineering) / unrestricted