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Thesis (Ph.D.)--University of Alberta, 2009. / A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Medical Sciences - Biomedical Engineering. Title from pdf file main screen (viewed on November 14, 2009). Includes bibliographical references.

Functional electrical stimulation (FES) systems have been developed to help restore various neuromuscular functions in individuals with neurological disorders leading to paralysis. Most of the current FES systems are designed for specific neuroprosthesis applications (i.e., walking, grasping, bladder voiding, coughing, etc.) and when one intends to use them in other custom made applications they are very limited due to a lack of functionality and flexibility in hardware and programmability. This prevents effective and efficient development of customized neuroprostheses. Research and development efforts at the Rehabilitation Engineering Laboratory at the University of Toronto were being carried out with an objective to produce a new, fully programmable and portable FES system. This thesis presents a novel proof-of-concept prototype controller for use in the new FES system. The controller subsystem manages and controls the overall FES system including the real-time decoding and execution of stimulation, data acquisition, external systems interfaces and user interface.

Functional electrical stimulation (FES) systems have been developed to help restore various neuromuscular functions in individuals with neurological disorders leading to paralysis. Most of the current FES systems are designed for specific neuroprosthesis applications (i.e., walking, grasping, bladder voiding, coughing, etc.) and when one intends to use them in other custom made applications they are very limited due to a lack of functionality and flexibility in hardware and programmability. This prevents effective and efficient development of customized neuroprostheses. Research and development efforts at the Rehabilitation Engineering Laboratory at the University of Toronto were being carried out with an objective to produce a new, fully programmable and portable FES system. This thesis presents a novel proof-of-concept prototype controller for use in the new FES system. The controller subsystem manages and controls the overall FES system including the real-time decoding and execution of stimulation, data acquisition, external systems interfaces and user interface.

The advancement of technologies that interface with electrically excitable tissues, such as the cortex and muscle, has the potential to lend greater mobility to the disabled, and facilitate the study of the central and peripheral nervous systems. Myoelectric interfaces are currently limited in their signal fidelity, spatial resolution, and interfacial area. Such interfaces are either implanted in muscle or applied to the surface of the muscle or skin. Thus far, the former technology has been limited in its applications due to the stiffness (several orders of magnitude greater than muscle) of its substrates, such as silicon and polyimide, whereas the latter technology suffers from poor spatial resolution and signal quality due to the physical separation between the electrodes and the signal source. We have developed a stretchable microneedle electrode array (sMEA) that can function while stretching and flexing with muscle tissue, thereby enabling multi-site muscle stimulation and electromyography (EMG) measurement across a large interfacial area. The scope of this research encompassed: (i) the development of a stretchable and flexible array of penetrating electrodes for the purposes of stimulating and measuring the electrical activity of excitable tissue, (ii) the characterization of the electrical, mechanical, and biocompatibility properties of this electrode array, (iii) the measurement of regional electrical activity of muscle via the electrode array, (iv) the study of the effect of spatially distributed stimulation of muscle on the fatigue and ripple of muscle contractions, and (v) the assessment of the extent to which the stretch response of electrically stimulated muscle behaves in a physiological manner.

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.

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