Doctor of Philosophy, Case Western Reserve University, 2023, Biomedical Engineering

Functional electrical stimulation (FES) of arm and hand muscles combined with decoded motor intentions from intracortical recordings can restore reaching ability to individuals with spinal cord injury. This research aims to improve FES generated arm movements by incorporating real-time modulation of limb stiffness in addition to limb kinematics. During normal arm movements people actively adjust the amount of cocontraction, or the degree to which opposing muscles are simultaneously active. Increasing cocontraction stiffens our limbs making them more resistant to external perturbations while decreasing cocontraction minimizes energy use and facilitates rapid movement. Optimizing the degree of cocontraction is particularly important for FES because excess cocontraction can lead to muscle fatigue and faster battery drain. Conversely, too little cocontraction can lead to poorly controlled movements that are prone to perturbations from the environment. First, we incorporated automated stiffness modulation into FES systems using a virtual arm model. After running many simulations, we found the optimal control method to automatically modulate limb stiffness based on the intended limb velocity command. Next, we built upon that method by addressing two key practical issues: 1) balancing stimulation across multiple redundant muscles, and 2) optimizing stimulation of biarticular muscles. With these advances, we demonstrate how to generate an upper limb FES control algorithm that is clinically practical to implement and allows one to control limb stiffness in addition to kinematics. Finally, with the goal of finding a neurally encoded volitional stiffness signal, we trained macaques with intracortical microelectrodes to perform an EMG controlled cursor task in which independent activation of antagonist muscles controlled one dimension and a separate dimension of movement was controlled by the cocontraction of the same muscles. Results revealed some neural signal (open full item for complete abstract)

Committee: Dawn Taylor (Advisor); Roger Quinn (Committee Member); A. Bolu Ajiboye (Committee Member); Robert Kirsch (Committee Chair) Subjects: Biomedical Engineering; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2022, Biomedical Engineering

Functional electrical stimulation (FES) can restore motor function to people with paralysis caused by spinal cord injuries (SCIs). Recently, deep neural networks (DNNs) trained with reinforcement learning (RL) have been explored as a promising methodology to control upper-limb FES systems. By emulating natural learning, RL may eliminate labor-intensive manual adjustments of controller parameters. However, previous studies did not consider musculoskeletal systems with highly fatigable and atrophied muscles, such as those observed in people with chronic paralysis. Here, we implemented a fatigable Hill-type musculoskeletal model to investigate the RL control of chronically paralyzed muscles. We discovered that RL controllers using Twin Delayed Deep Deterministic Policy Gradients (TD3) and Hindsight Experience Replay (HER) could effectively control a fatigable horizontal planar model of the human arm, as long as the arm was given sufficient rest between motor tasks. Also, muscle weakness and increased muscle stiffness caused by chronic paralysis considerably decreased the workspace of the arm. Finally, by incorporating negatively biased layer normalization, it was possible to decrease the muscle activations commanded by RL controllers while maintaining high performance. The results in the present study support the feasibility of using RL control to restore upper-limb motor function to people with SCIs, and we hope that they will inform the design of effective FES controllers that can be more easily translated into clinical practice.

Committee: Robert Kirsch (Advisor) Subjects: Biomechanics; Biomedical Engineering; Biomedical Research; Rehabilitation

Doctor of Philosophy, Case Western Reserve University, 2021, Biomedical Engineering

Cervical level spinal cord injuries often result in paralysis of all four limbs - a condition known as tetraplegia. Tetraplegia severely limits patient independence and quality of life. Previous studies have demonstrated that coordinated functional electrical stimulation (FES) of the neuromuscular system can restore limited motor function to people with tetraplegia. However, to fully restore upper-limb motor function, controllers for FES systems must be able to coordinate the many actuators and many mechanical degrees of freedom of the human upper extremity. Several FES controller architectures have already been identified. However, most of these architectures required patient-specific manual tuning, which may not be practical to perform for all 175,000 people in the United States living with tetraplegia due to spinal cord injuries. Here, I propose an FES controller for the human upper extremity that learns automatically via reinforcement learning without the need for patient-specific manual tuning. I demonstrate that the reinforcement learning controller can quickly learn to control a horizontal planar model of the human arm with high accuracy. In the future, I hope that reinforcement learning controllers will enable efficient and efficacious restoration of motor function to people with spinal cord injuries.

Committee: Robert Kirsch PhD (Advisor); Jonathan Miller MD (Committee Member); Dustin Tyler PhD (Committee Member); Antonie van den Bogert PhD (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Engineering

Master of Science (M.S.), University of Dayton, 2020, Mechanical Engineering

This thesis presents an investigation of alternative mechanisms to improve the power throughput of persons with tetra- or paraplegia pedaling via functional electrical stimulation (FES). FES stimulates muscle contraction with small electrical currents and has proven useful in building muscle in patients while relieving soreness and promoting cardiovascular health. An FES-stimulated cyclist produces power that is an order of magnitude less than an able-bodied cyclist. At these reduced power levels, many difficulties associated with FES cycling become apparent, namely inactive zones. Inactive zones are defined by the leg being in a position where muscle stimulation is unable to produce power to propel the tricycle forward. A possibility for reducing inactive zones and increasing the power throughput of the cyclist is to alter the motion of a cyclist's legs. Bicycles have recently been marketed that feature pedaling mechanisms that employ alternate leg motions. This work considers using four-bar and ratchet-and-pawl linkages in the redesign of a performance tricycle piloted by an FES-stimulated rider. Quasi-static and power models have been optimized for this cycling architecture yielding design that suggest a 79% increase in throughput power for some FES cyclist. Multiple designs were compared against design criteria to identify an ideal design.

Committee: Andrew Murry Ph.D. (Committee Chair); David Myszka Ph.D. (Committee Member); Timothy Reissman Ph.D. (Committee Member) Subjects: Biomechanics; Mechanical Engineering

Doctor of Philosophy in Engineering, Cleveland State University, 2020, Washkewicz College of Engineering

Functional electrical stimulation (FES) is a promising technique for restoring the ability to complete reaching motions to individuals with tetraplegia due to a spinal cord injury (SCI). FES has proven to be a successful technique for controlling many functional tasks such as grasping, standing, and even limited walking. However, translating these successes to reaching motions has proven difficult due to the complexity of the arm and the goal-directed nature of reaching motions. The state-of-the-art systems either use robots to assist the FES-driven reaching motions or control the arm of healthy subjects to complete planar motions. These controllers do not directly translate to controlling the full-arm of an individual with tetraplegia because the muscle capabilities of individuals with spinal cord injuries are unique and often limited due to muscle atrophy and the loss of function caused by lower motor neuron damage. This dissertation aims to develop a full-arm FES-driven reaching controller that is capable of achieving 3D reaching motions in an individual with a spinal cord injury. Aim 1 was to develop a complete-arm FES-driven reaching controller that can hold static hand positions for an individual with high tetraplegia due to SCI. We developed a combined feedforward-feedback controller which used the subject-specific model to automatically determine the muscle stimulation commands necessary to hold a desired static hand position. Aim 2 was to develop a subject-specific model-based control strategy to use FES to drive the arm of an individual with high tetraplegia due to SCI along a desired path in the subject's workspace. We used trajectory optimization to find feasible trajectories which explicitly account for the unique muscle characteristics and the simulated arm dynamics of our subject with tetraplegia. We then developed a model predictive control controller to control the arm along the desired trajectory. The controller developed in this dissertat (open full item for complete abstract)

Committee: Eric Schearer PhD (Advisor); Hanz Richter PhD (Committee Member); Antonie van den Bogert PhD (Committee Member); Deborah Espy PT, PhD (Committee Member); Levi Hargrove PhD (Committee Member) Subjects: Mechanical Engineering; Rehabilitation

Doctor of Philosophy, Case Western Reserve University, 2020, Biomedical Engineering

The objective of this project was to explore the use of myoelectric signals generated from muscles below the SCI level as command sources for a neuroprosthetic system. Using functional electrical stimulation, motor neuroprostheses can restore function after paralysis caused by spinal cord injury (SCI). Command signals derived from the user's volitional intent are required to control these devices. In current systems, command is provided by myoelectric activity from muscles above the injury level. For improved functional capabilities, advanced neuroprosthetic technology demands more command signals than are conventionally available. Previous studies suggest that axonal sparing is common even in injuries diagnosed as motor complete, in which no visible signs of muscle activity below the injury are observed. As a result of this sparing, it is possible, even in the absence of visible movement, for movement attempts to produce myoelectric activity detectable via electromyographic (EMG) sensors. This myoelectric activity could provide an innovative source for neuroprosthetic control. To characterize the prevalence of this activity, surface EMG recordings from lower-extremity muscles were performed during volitional movement attempts in individuals with motor-complete SCI. Significant below-injury muscle activity was identified in the majority of participants, with a smaller proportion producing high-quality signals which we theorized capable of providing neuroprosthetic control. To support this theory, as a proof-of-concept we demonstrated the successful control of an implanted hand grasp neuroprosthesis via EMG signals from the participant's toe flexor. This feasibility test, which included functional grasp measures, demonstrates the potential for below-injury signals to provide a novel form of neuroprosthesis control. Lastly, we implemented a biofeedback training protocol with the goal of improving signal quality from muscles which contained significant, but no (open full item for complete abstract)

Committee: P. Hunter Peckham PhD (Advisor); A. Bolu Ajiboye PhD (Committee Chair); Kevin Kilgore PhD (Committee Member); Warren Alilain PhD (Committee Member); Michael Keith MD (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2020, Biomedical Engineering

In upper limb amputation, sensory feedback from the hand is lost, significantly reducing users' ability to interact with the environment, even with a prosthesis. Sensation location, one of four basic dimensions of somatosensation, provides valuable information about how the hand interacts with objects or the environment. Location is encoded by the receptive fields of individual axons and then cortically processed to create the perception of touch in the intact system. We hypothesized that artificial somatosensory locations are encoded, processed, and utilized similarly to intact somatosensory locations. We quantified the effects of various electrical and functional conditions on the location and functional use of evoked sensory percepts. Patient-specific computational and statistical models probed the underlying mechanisms driving experimental outputs. We found that peripheral nerves of the upper arm retain a somatotopic organization proximal to the elbow. Consequently, cuff electrodes, which recruit spatially grouped axon populations, encode focal percepts across targeted regions of the hand independent of placement along the peripheral nerve. However, functional use of the arm can induce some variability to the cuff-nerve interface. This changes the size of the active axon population and, correspondingly, the size of the perceived sensation location, as a prosthesis is actively used. We also found that perception of artificial sensation is not solely dependent on the active axon population. Instead, like intact perception, top-down modulators influence tactile perception. Perceived sensory locations are more stable and more aligned with expected sensory locations when tested in a functional context. Further, this alignment becomes permanent with prolonged exposure to functionally-relevant artificial somatosensation over months of take-home usage. Such changes are dependent on the transduction of meaningful information through the percept; sensory locations (open full item for complete abstract)

Committee: Dustin Tyler PhD (Advisor); Kenneth Gustafson PhD (Committee Chair); Robert Kirsch PhD (Committee Member); Vira Chankong PhD (Committee Member) Subjects: Biomedical Engineering; Neurosciences; Rehabilitation

Doctor of Philosophy, Case Western Reserve University, 2018, Biomedical Engineering

Functional Electrical Stimulation (FES) is an assistive technology that uses stimulating electrodes to reanimate muscles and restore lost functions to people with tetraplegia. Brain-computer interfaces (BCIs), which decode recorded neural activity into user commands, are an enticing technology for commanding assistive devices because they can extract multiple command signals even in the absence of movement. This work, through the BrainGate2 pilot clinical trial, makes progress towards restoring brain-controlled arm and hand movements after paralysis through three main advancements in the development of a combined FES+BCI system. First, we show that FES stimulation produces electrical artifacts on intracortical recordings that significantly degrades BCI performance, particularly in the case of surface FES. However, we present a novel artifact reduction method, linear regression reference, which extracts meaningful information during both implanted and surface FES periods and fully restores normal BCI performance. Second, we compare two potential interfaces for control of human arm reaching: Cartesian and joint based commands. In a virtual reaching and posture matching task, we analyze neural tuning differences between the conditions and show significantly higher performance when using the standard Cartesian commands. Third, we implement the first BCI control of an implanted FES system for restoring four dimensions of arm and hand movement. We show evidence that neural activity is largely similar between control of real and virtual movements, and demonstrate similar performance in FES tasks compared to virtual training. We make progress towards restoring brain-controlled movement after paralysis through improvements in three components of an FES+BCI system: the signal processing, command interface, and effector. Our work culminates in a demonstration of functional performance through one participant's consistent success in self-initiated activities of dail (open full item for complete abstract)

Committee: A. Bolu Ajiboye PhD (Advisor); Robert Kirsch PhD (Committee Chair); Dawn Taylor PhD (Committee Member); Jonathan Miller MD (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Rehabilitation

Master of Science in Biomedical Engineering, Cleveland State University, 2018, Washkewicz College of Engineering

Functional electrical stimulation (FES) is a technology capable of improving the quality of life for those with the loss of limb movement related to spinal cord injuries. Individuals with high-level tetraplegia, in particular, have lost all movement capabilities below the neck. FES has shown promise in bypassing spinal cord damage by sending electrical impulses directly to a nerve or muscle to trigger a desired function. Despite advancements in FES, full-arm reaching motions have not been achieved, leaving patients unable to perform fundamental tasks such as eating and grooming. To overcome the inability in current FES models to achieve multi-joint coordination, a controller utilizing muscle activations to achieve full-arm reaching motions using equilibrium point control on a computer-simulated human arm was developed. Initial simulations performed on the virtual arm generated muscle activations and joint torques required to hold a static position. This data was used as a model for Gaussian Process Regression to obtain muscle activations required to hold any desired static position. The accuracy of the controller was tested on twenty joint positions and was capable of holding the virtual arm within a mean of 1.1 ± 0.13 cm from an original target position. Once held in a static position, external forces were introduced to the simulation to evaluate if muscle activations returned the arm towards the original position after being moved away within a basin of attraction. It was found that the basin of attraction was limited to a 15 cm sphere around the joint position, regardless of position in the workspace. Muscle activations were then tested and found to successfully perform movements between points within the basin. The development of a controller capable of equilibrium point controlled movement is the initial step in recreating these movements in high-level tetraplegia patients with an implanted FES.

Committee: Eric Schearer Dr. (Advisor); Dan Simon Dr. (Committee Member); Antonie van den Bogert Dr. (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Biomedical Research

Master of Science, The Ohio State University, 2018, Biomedical Engineering

Approximately 285,000 people are living with a Spinal Cord Injury (SCI) in the United States alone and there are about 17,500 additional cases each year. Over half of these SCI cases result in tetraplegia, which impairs quality of life and requires the need for self-care assistance. Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. There are multiple groups working to develop BCIs for SCI applications and incredible progress has been accomplished. However, there is still a substantial amount of research and development required to optimize the technology in order for people with tetraplegia to integrate the neurorehabilitation devices into their daily lives. The work presented in this thesis aims to (I) translate BCI- FES technology from research devices to clinical neuroprosthetics, (II) enhance decoder performance through optimal selection of neurally separable hand functions, and (III) improve neurorehabilitation BCI-FES systems through integration of error-based feedback. Three studies were conducted with a tetraplegic participant using an intracortically-controlled, transcutaneous FES system designed for motor recovery to address each aim. We demonstrate that (I) our BCI-FES system can enable seven functional, skilled hand grasps that can generate adequate force to manipulate everyday objects with high-precision and naturalist speed, (II) stable representations of different hand movements can form in a very small area of the motor cortex and discriminability between these neural representations can affect decoder performance, and (III) information regarding mismatches between motor intention and muscle activation in a tetraplegic participant using a BCI-FES is expressed through single (open full item for complete abstract)

Committee: Marcia Bockbrader MD, PhD (Advisor); Thomas Hund PhD (Committee Member) Subjects: Biomedical Engineering; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2018, Biomedical Engineering

Peripheral nerve cuff electrodes (NCEs) have been deployed in neuroprostheses restoring or modulating motor, sensory, and autonomic functions. They address myriad pathologies including stroke, spinal cord injury, amputation, seizure, chronic pain. As these applications encompass more indications, NCEs may be deployed in more anatomically-challenging locations while still delivering selective and stable stimulation and preserving the health of the implanted nerves. A novel class of reshaping electrodes with patterned regions of stiffness enable implantation in a widening range of anatomical locations. Patterning stiff regions and flexible regions of the electrode enables nerve reshaping while accommodating anatomical constraints of various implant locations ranging from peripheral nerves to spinal and autonomic plexi. We introduce the composite flat interface nerve electrode (C-FINE), flexible and small enough to be suitable for implantation near joints or other constrained locations. Benchtop testing verified the C-FINE does not exert ischemia-inducing pressure on nerves, even in the face of potential nerve swelling. Animal testing verified safety of C-FINE shells implanted on peripheral nerves for 3 months through a combination of nerve conduction studies and quantitative histology. Classically, this benchtop and animal testing followed by chronic observation of neuroprosthesis function have served as a proxy for directly measuring nerve health. We implanted the first-in-man C-FINEs on the proximal femoral nerves near the inguinal ligament of a man with cervical spinal cord injury. Over the first year of implantation we established the safety of C-FINEs in this anatomically constrained location directly via clinical electrodiagnostics. Future NCE designs can use these clinical results as a baseline for expected changes in a well-functioning neuroprosthesis. Previous NCEs have not been able to selectively activate hip flexors and knee extensors when i (open full item for complete abstract)

Committee: Ronald Triolo PhD (Advisor); Dustin Tyler PhD (Advisor); Dominique Durand PhD (Committee Chair); Stephen Selkirk MD/PhD (Committee Member) Subjects: Biomedical Engineering; Neurology

Doctor of Philosophy, Case Western Reserve University, 2017, Biomedical Engineering

Intracortical brain-computer interfaces (iBCIs) can help to restore movement and communication to people with chronic tetraplegia by recording neural activity from the motor cortex and translating it into the motion of an external device (typically a computer cursor or robotic arm). In this work, we focus on three avenues for advancement: (1) better understanding the feedback control loop created by the interaction between the user and the iBCI, (2) leveraging that understanding to improve the performance of decoding algorithms that translate neural activity into movement, and (3) restoring control over a person's own arm and hand by using a combined iBCI and muscle stimulation system. In Chapters 2-3, we use data from the BrainGate2 pilot clinical trial to develop a feedback control model that describes how users modulate their neural activity to move towards their target, stop accurately, and correct for movement errors when using a linear decoder. We characterize the decoding errors we observed and show how they cause iBCI movements to differ from able-bodied movements. In Chapters 4-6, we explore three avenues for improving decoder performance based on our findings from Chapters 2-3. First, we improve the standard linear decoder by adding a separate decoding pathway that can extract non-linear movement scale information from the neural activity. Second, we show that our feedback control model can be used to optimize decoder performance by predicting which parameters will lead to the best closed-loop performance. Third, we test whether our feedback control model can improve decoder calibration by more accurately estimating the user's intended movements. In Chapters 7-8, we make progress towards a combined iBCI and functional electrical stimulation (FES) system that can restore motion to a person's own arm and hand. In a non-human primate model, we develop and test a new decoding method that enables direct cortical control over muscle stimulation and that can be (open full item for complete abstract)

Committee: Abidemi Ajiboye Dr. (Advisor); Dawn Taylor Dr. (Advisor); Robert Kirsch Dr. (Committee Chair); Michael Lewicki Dr. (Committee Member) Subjects: Biomedical Engineering; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2015, Biomedical Engineering

The overall goal of this work is to optimize nerve cuff stimulation for selective activation of upper extremity nerves. The characterization of upper extremity nerve dimensions is important for electrode design development. Quantitative measures such as nerve diameter, number of fascicles, and fascicle diameters were used to guide neural electrode dimensions. The quantitative upper extremity measurements were used as a template to create upper extremity simulation models. We constructed physiologically based Finite Element Method (FEM) models of nerve cuff electrodes at low, moderate, and high contact densities at 16 nerve locations in median, ulnar, and radial nerves. We hypothesize that adding two flanking anodes to an active cathode is sufficient for optimal selectivity of fascicles in upper extremity nerves. We exhaustively tested one and two channel configurations, as well as, all three channel configuration within six contacts. Since the number of permutations of stimulation parameters increases exponentially by adding anodes, a genetic algorithm search routine was employed. Seventy-nine percent of all fascicles were selectively activated with high density electrodes and multiple channel stimulation. Only 2.5% of selective fascicles required more than 2 anodes in the stimulation configuration. The important implication of this work is that optimal system design requires high density nerve cuff electrodes, but no more than four simultaneously active stimulation channels routed through a multiplexor. We tested the capabilities of a high density electrode with multipolar stimulation in non-human primate upper extremity nerves. A high density Composite Flat Interface Nerve Electrode (CFINE) was implanted chronically in a non-human primate on the median, radial, and ulnar nerves. Electromyography (EMG) recordings were used to optimize nerve stimulation parameters to increase selective muscle activation of the hand and arm muscles using high density nerve cuf (open full item for complete abstract)

Committee: Dustin Tyler Ph.D. (Advisor); Robert Kirsch Ph.D. (Committee Member); Kevin Kilgore Ph.D. (Committee Member); Vira Chankong Ph.D. (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2014, Biomedical Engineering

High-level spinal cord injury causes paralysis below the level of the neck. Functional Electrical Stimulation (FES) is a technology that restores voluntary movement via application of electrical current to nerves and muscles. Our work aims to restore movement in the paralyzed upper limb. When implementing FES systems, effective controllers are needed to translate the current and desired arm positions into a pattern of muscle stimulations that achieve the target position accurately and efficiently. Although a range of upper-extremity neuroprosthesis controllers exist, none is capable of restoring accurate, natural arm movement in a clinical setting. For the purpose of advancing upper-extremity FES control technology, we explore reinforcement learning (RL), a control strategy that uses delayed reward and a trial-and-error search to develop its action policy. A potential advantage of RL control for upper-extremity FES systems is that human user preferences can be incorporated into controller training through the use of user-generated rewards of the controller actions. To date, RL control has been minimally explored for FES systems, and human rewards have never been incorporated for this application. An RL controller was implemented for a planar 2 degree of freedom biomechanical arm model, and this project explored the feasibility of using human rewards to train the RL controller. Simulation experiments were performed using pseudo-human, computer generated rewards that simulate the rewards that a human would be likely to assign. A range of experiments was performed to examine the learning properties of RL control using human-like rewards, and it was determined that RL controller learning occurs over a measurable time frame. Subsequently, human rewards were introduced to train the RL controller. Ten human subjects viewed animations of arm reaching movements, and assigned rewards to train the RL controller based on the quality of each movement. The RL controllers (open full item for complete abstract)

Committee: Robert Kirsch Ph.D. (Advisor); Antonie van den Bogert Ph.D. (Committee Member); Dawn Taylor Ph.D. (Committee Member); Kenneth Gustafson Ph.D. (Committee Member) Subjects: Artificial Intelligence; Behavioral Psychology; Behavioral Sciences; Biomedical Engineering; Computer Engineering; Computer Science; Engineering; Health Care; Psychology; Rehabilitation

Doctor of Philosophy, Case Western Reserve University, 2014, Biomedical Engineering

Using a model-based approach, we designed and evaluated nonlinear combined feedforward-feedback algorithms to control arm movements in the presence of a wide range of perturbations: (1) manipulation of objects of unknown mass, (2) sensor noise, (3) muscle fatigue, (4) model uncertainty with respect to the true inertial properties of the arm and the true muscle dynamics, and (5) input time delays. These algorithms were developed specifically for use in FES neuroprostheses for individuals with paralysis due to spinal cord injury and other neurological disorders. An initial comparison of combined feedforward-feedback proportional-derivative (PD), adaptive control, and sliding mode control showed that input time delays quickly caused instability for all three controllers if feedback gains were chosen too high. Decreasing the feedback gains (i.e., shifting towards feedforward control) re-established stability, but greatly reduced performance as measured by the root-mean-square error between the specified movement intent and the simulated joint angles. Input time delays are unavoidable in FES applications, arising from the muscle dynamics and the relatively low stimulation frequency (i.e. 12Hz) typically used in upper extremity FES neuroprostheses. The destabilizing effects of time delay therefore cannot be ignored. Using a 2DOF musculoskeletal arm model, we designed and evaluated a nonlinear combined feedforward-feedback controller with time delay compensation. In the presence of a typical 80ms time delay, this controller achieved excellent tracking accuracy, both under ideal conditions (shoulder RMSE: 0.27º, elbow RMSE: 0.62º) and in the presence of a wide variety of perturbations expected under normal operating conditions (shoulder RMSE: 2.99º, elbow RMSE: 5.15º). We extended this time delay compensating controller to a more functionally relevant 5DOF arm model. This extended controller achieved stable, accurate tracking, even in the presence of time delay, measurement (open full item for complete abstract)

Committee: Robert Kirsch (Advisor); Patrick Crago (Committee Member); Antonie van den Bogert (Committee Member); Frans van der Helm (Committee Member); Wei Lin (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, The Ohio State University, 2004, Biomedical Engineering

Using blood oxygen level dependent functional magnetic resonance imaging (BOLD FMRI), the brain areas activated by pain were studied. These initial studies lead to interesting new findings in the body's response to pain and to the refinement of one method used in FMRI analysis for correction of physiologic noise (signal fluctuations caused by the cyclic and non-cyclic changes in the cardiovascular and respiratory status of the body). These investigations will be summarized below. In the first study, evidence was provided suggesting that the multiple painful stimulations used in typical pain FMRI block designs causes attenuation over time of the BOLD signal within activated areas. The demonstrated BOLD attenuation seems unique to pain studies. One possible explanations is that changing hemodynamics caused by a physiologic response to pain alter the BOLD response. The next study began the investigation of this by monitoring the physiologic response to pain for eight subjects. It was found that respiratory rate and tidal volume increased, while heart rate, cardiac output, end-tidal carbon dioxide levels, and global cerebral blood flow all decreased. The cause of these changes appears to be a combination of short-lived sympathetic and long lasting parasympathetic nervous system activations. It is well established that changes in respiration and global cerebral blood flow can affect the BOLD response, leading to the final investigation of this dissertation. Heart rate, respiratory rate and depth, and end-tidal carbon dioxide levels were collected during a BOLD FMRI study of pain. A new technique for removing signal that covaries with the actual breathing values present during the collection of each image and with end-tidal carbon dioxide levels was introduced. This technique showed in increase in model fit of 85%, and the functional maps showed an average increase in the number of activated pixels of 6.53% over the eight subjects. Including the breathing and end-tidal ca (open full item for complete abstract)

Committee: Petra Schmalbrock (Advisor) Subjects:

Master of Sciences, Case Western Reserve University, 2012, Biomedical Engineering

With the help of functional electrical stimulation (FES) systems, people with high level spinal cord injuries have been able to regain sufficient control of upper limb movement to engage in basic acts of daily living such as feeding and grooming. However, the difficulty of expressing complex movement intention through current command systems for these devices remains a significant limitation of FES technology. Coadaptive algorithms are an effective method of decoding user intent from motor cortex activity, but there has been no previous attempt to use such a system to decode muscle activation outputs directly from motor cortex activity for use with an FES system. In this study, such a decoder was developed and tested. The coadaptive decoding algorithm described here successfully mapped motor cortex activity directly to muscle activation levels in a way that enabled real-time control of a simulated FES system in a horizontal workspace using only brain activity.

Committee: Robert Kirsch PhD (Committee Chair); Dawn Taylor PhD (Advisor); Cameron McIntyre PhD (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2012, Biomedical Engineering

Functional neuromuscular stimulation (FNS) is an important intervention in improving quality of life for individuals with spinal cord injury (SCI). For those with thoracic level injuries, FNS can restore standing and allow for significantly enhanced mobility. While these systems can facilitate short duration activities like transferring from one surface to another, their utility for longer duration standing has been inconsistent. Some users can stand for an hour or longer, while most are limited to five minutes or less, usually because of muscle fatigue and buckling at the knee joints. Multi-contact electrodes have the potential to increase muscle recruitment and improve the performance of FNS systems for standing after SCI. By selectively activating multiple populations of motor units within the quadriceps, these electrodes can more completely activate the muscle tissue, while also allowing for stimulation paradigms that delay the onset of fatigue. In this study, we replaced the muscle-based electrode typically used to activate the knee extensors in an FNS system for standing after SCI with a four contact spiral nerve-cuff electrode wrapped around the femoral nerve. We demonstrated that these electrodes can achieve a stable interface with the nerve and produce strong contractions that are sufficient to lock the knees during standing. In one long-time user of the FNS system, we demonstrated a threefold increase in standing time by replacing the muscle-based electrode with a spiral nerve-cuff electrode. We also demonstrated the selectivity of these electrodes in stimulating multiple independent populations of motor units within the quadriceps. We developed an efficient method for optimizing stimulation parameters for high density multi-contact stimulating electrodes to produce the strongest possible contractions with the least possible overlap between contacts. In three subjects, we demonstrated that the nerve-cuff electrodes could selectively activate independent po (open full item for complete abstract)

Committee: Ronald J. Triolo PhD (Advisor); Patrick E. Crago PhD (Committee Chair); Dustin J. Tyler PhD (Committee Member); Stephen M. Selkirk MD, PhD (Committee Member) Subjects: Biomechanics; Biomedical Engineering; Rehabilitation

Doctor of Philosophy, Case Western Reserve University, 2011, Biomedical Engineering

A flat interface nerve electrode (FINE) is a type of nerve cuff electrode designed to increase spatial selectivity by reshaping a nerve and realigning fascicles inside the nerve trunk. FINE has shown improved fascicular and subfascicular selectivity enabling the activation of many muscles with a single electrode. Despite significant improvement in the spatial selectivity of FINE, a motion control algorithm for Functional Electrical Stimulation (FES) using a FINE has not yet been developed, due to the inherent complexities in neuromuscular skeletal systems and the nerve-electrode interface. In this study, a novel motion control algorithm is presented to overcome these difficulties. The proposed control method does not require an analytical model of the system being controlled. Therefore, this control method is suitable for FES control with a FINE, because finding an accurate model with FINE is extremely difficult. In order to test the performance of the control algorithm, a computational model of the human ankle joint system was developed along with a finite element model of the sciatic nerve. The results indicate that both the ankle and subtalar joint motion could be controlled using a twenty-contact FINE on the sciatic nerve. The proposed controller was also tested in an acute rabbit experimental study, and the result showed good reference trajectory tracking performance within 10% average RMS error. The results in both computational simulation study and animal experiment study show that this novel control method is suitable for the system where a model is not available and where the feedback signals are difficult to obtain.

Committee: Dominique Durand (Advisor); Patrick Crago (Committee Member); Robert Kirsch (Committee Member); Dustin Tyler (Committee Member); Kenneth Loparo (Committee Member); Kenneth Gustafson (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy, Case Western Reserve University, 2010, Biomedical Engineering

Assistive devices, such as motor neuroprostheses, have been developed to help restore function for individuals with tetraplegia. For individuals with severe paralysis, command sources for assistive devices are limited to muscle activity and/or movements of the head and face. These commands can impede eating, talking, and other activities. Incorporating signals from the brain may be a valuable way to augment the command options for complex devices. The attempted movements of body parts generate characteristic changes in field potentials over brain areas associated with those body parts. These movement-related changes can be recorded from the scalp and used to control an assistive device. Most previous studies using movement-related field potentials as a command source have been focused on the abstract control of computer cursors and not specifically focused on restoring arm and hand function using neuroprostheses. We developed new spatial filtering techniques to help separate the cortical activities associated with the movement and rest of different body parts. Using these novel spatial filters, we demonstrated that two-dimensional movement of a ‘virtual upper-extremity neuroprosthesis' can be controlled using electroencephalography (EEG) signals that are modulated by the attempted movement of two body parts which are spread apart in the motor homunculus (i.e. hand and feet). The attempted movement of the feet and hand was an abstract command strategy using body parts unrelated to the desired device movement. A more natural command strategy of using the attempted movements of arm and hand joints was evaluated as a more intuitive way to control the same joints of the neuroprosthesis. Using a more natural command strategy, we demonstrated that individuals with tetraplegia were able to intuitively control the grasp of a virtual hand using movement-related field potentials associated with hand extension and relaxation. When expanding intuitive control to combinations of (open full item for complete abstract)

Committee: Dawn M. Taylor PhD (Advisor); Robert Kirsch PhD (Committee Chair); Cameron McIntyre PhD (Committee Member); Kenneth Gustafson PhD (Committee Member); Wojbor Woyczynski PhD (Committee Member) Subjects: Biomedical Engineering; Neurosciences; Rehabilitation