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For several decades of years, the electrical stimulation has been applied on rehabilitation of motional recovery for quadriplegic and paraplegic patients such as walking, standing, and cycling exercise. As the advancement of VLSI (very large scale integration) technology, the implantable micro-stimulators become feasible in recent years. This thesis presents an implantable system including an inductively coupling transceiver of power & data, a protocol of communication, and the implementation of a FES (Functional Electrical Stimulation ) SOC (System-On-chip). The first part of this thesis discusses the architecture of the proposed implantable FES system, including the theory of wireless power transmission, the implementation of mixed-signal circuits, the RS232 protocol, and two encoding methods of Manchester code and NRZ code. The second part of this thesis is focused on the multi-frequency stimulation of the implantable FES system, which comprises an advanced communication protocol suitable for multi-frequency stimulation function and a novel arrangement of interconnections for the chip.

Electrical potentials are the fundamental currency of communication in the nervous system. The advanced executive functions of the prefrontal cortex and the motor commands delivered to the neuromuscular junction, though involved with very different aspects of behavior, both rely on time-varying electrical signals. It is possible to "listen to" the internal communications of the nervous system by measuring the electrical potentials in the extra-cellular space. However, this is only meaningful if there is some way to interpret these signals, which are incredibly complicated and information rich. This dissertation represents an attempt to decode some of these signals in order to reveal their significance for behavior and function. The first study is an investigation of the relationship between different elements of the local field potential in the prefrontal cortex and memory consolidation. It is shown that certain electrographic signatures of non-rapid eye movement sleep, namely K-complexes and low-voltage spindles, are correlated with neuronal replay of recent experiences. It is also shown that the global fluctuations of activity in the population of cells, known as up/down states, is correlated with neuronal replay. Finally, it is shown that high-voltage spindles are not correlated with memory replay, and are therefore functionally different from low-voltage spindles. The second study focuses on the relationship between movements of the upper limb and the coordinated neural control, as measured by the electromyogram (EMG), of the muscles generating that movement. We show that different probability-based models can be used to predict what the pattern of EMG in the different muscles will be for any given kinematic state of the hand. In the third study it is demonstrated that the kinematic output associated with a particular pattern of EMG can be reproduced with electrical stimulation. Thus, it is not only possible to understand the commands issued by the nervous system, it is also possible to issue commands by interfacing with the nervous system directly. Finally, the design for an experiment that would combine EMG prediction with translation of EMG into electrical stimulus patterns is presented. The objective of this study would be to use these methods to fully control the upper limb in a way that would be useful for a functional electrical stimulation-based neuroprosthetic for spinal cord injured patients.

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.

Recovery of the ability to maintain balance during standing is one of the primary and essential goals of rehabilitation programs for individuals with Spinal Cord Injury (SCI). Regaining functionality during standing by means of a neuroprosthesis would decrease secondary complications and increase independence, and would consequently improve the quality of life of these individuals. However, the development of a standing neuro- prosthesis requires techniques to generate reliable and predictable torque vectors in the lower limbs. We proposed and tested a method based on surface Functional Electrical Stimulation (FES) and the idea that three independent muscles can form a basis that would span the joint torque vector space. We tested the proposed stimulation technique on the quadriceps muscles that produce knee extension. The results of this study suggest that the quadriceps muscle basis vectors are insufficient to cover the knee joint vector space.

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.

Recovery of the ability to maintain balance during standing is one of the primary and essential goals of rehabilitation programs for individuals with Spinal Cord Injury (SCI). Regaining functionality during standing by means of a neuroprosthesis would decrease secondary complications and increase independence, and would consequently improve the quality of life of these individuals. However, the development of a standing neuro- prosthesis requires techniques to generate reliable and predictable torque vectors in the lower limbs. We proposed and tested a method based on surface Functional Electrical Stimulation (FES) and the idea that three independent muscles can form a basis that would span the joint torque vector space. We tested the proposed stimulation technique on the quadriceps muscles that produce knee extension. The results of this study suggest that the quadriceps muscle basis vectors are insufficient to cover the knee joint vector space.

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A forward dynamic model was developed to predict the performance of Spinal Cord Injured (SCI) individuals cycling an isokinetic ergometer using Neuromuscular Electrical Stimulation (NMES) to elicit contractions of the quadriceps, hamstring and gluteal muscles. Computer simulations were performed using three inter-connected models: a kinematic model of segmental linkages, a muscle model predicting forces in response to stimulation, and a kinetic model predicting ergometer pedal forces resulting from muscle stimulation. Specific model parameters for SCI individuals were determined through measurements from isometric and isokinetic contractions of the quadriceps muscles elicited using surface stimulation. The muscle model was fitted to data resulting from these isolated experiments in order to tailor the model's parameters to characteristics of muscles from SCI individuals. Isometric data from a range of knee angles were used to fit tendon slack lengths to the rectus femoris and vastus muscles. Adjustments to the quadriceps moment arm function were not able to improve the match between measured and modelled knee extension torques beyond those using moment arms taken from available literature. Similarly, literature values for constants from the muscle force - velocity relationship provided a satisfactory fit to the decline in torque with angular velocity, and parameter fitting did not improve this fit. Passive visco-elastic resistance remained constant for all velocities of extension except the highest (240 deg/s). Since knee angular velocities this high were not experienced during cycling, a visco-elastic dampener was not included within the present cycling model. The rise and fall in torque following NMES onset and cessation were used to fit constants to match the rate of change in torque. Constants for the rise in torque following NMES onset were significantly altered by changes in knee angle, with more extended angles taking longer for torque to rise. This effect was small, however, within the range of angles used during cycling, and consequently was not included within the cycling model. The decline in torque after NMES cessation was not affected by knee angle. A period of five minutes cyclical isometric activity of the quadriceps resulted in torque declining by more than 75% from rested levels. The activation time constants were largely unaffected by this fatigue, however, with only a small increase in the time for torque to decline, and no change in rise time or the delay between stimulation changes and resulting torque changes. The cycling model, therefore, did not incorporate any effect for changes in activation timing with fatigue. Performance of the full model was evaluated through measurements taken from SCI individuals cycling a constant velocity ergometer using NMES elicited contractions of the quadriceps, hamstring and gluteal muscles. Pedal transducers measured forces applied to the pedals for comparison between measured and modelled values. A five minute period of continuous cycling using just the quadriceps muscles produced similar results to those found for isolated knee extension. External power output dropped by 50% over the five-minute period, however there was no change in the pattern of torque production with fatigue. Cycling experiments were conducted using single muscle groups across a range of NMES firing angles. Experimental protocols were designed to seek the firing angles for each muscle that maximised power output by that group. Changes in power output in response to firing angle changes were not large, however, in comparison to the effects of cumulative fatigue and inconsistent power output between trials. This lead to large uncertainties in the determination of those firing angles that maximised power output by each muscle. Results suggest that NMES firing angles to maximise power output by the quadriceps muscles were relatively similar for each subject. For the hamstring muscles, however, substantial differences were observed in the range of firing angles that maximised power output. Results for the gluteal muscles were variable, with some subjects not applying any measurable torque to the cranks, even with maximal stimulation applied. The model produced a good match to experimental data for the quadriceps muscles, both in the shape of pedal force curves and the firing angles that maximised external power output. The individual variability in hamstring responses was not, however, predicted by the model. Modification of the relative size of the hamstrings' moment arms about the hip and knee substantially improved the match between measured and modelled data. Analysis of results suggests that individual variability in the relative size of these moment arms is a major cause of variation in individual's response to hamstring stimulation. There were apparent limitations in the model's ability to predict the shape of crank torques resulting from stimulation of the gluteus maximus muscle. It is suggested that further research be conducted to enable modelling of this muscle using a range of fibre lengths and moment arms.