Adaptive parameter selection for asynchronous intrafascicular multi-electrode stimulation

pre-printThis paper describes an adaptive algorithm for selecting perelectrode stimulus intensities and inter-electrode stimulation phasing to achieve desired isometric plantar-flexion forces via asynchronous, intrafascicular multi-electrode stimulation. The algorithm employed a linear model of force production and a gradient descent approach for updating the parameters of the model. The adaptively selected model stimulation parameters were validated in experiments in which stimulation was delivered via a Utah Slanted Electrode Array that was acutely implanted in the sciatic nerve of an anesthetized feline. In simulations and experiments, desired steps in force were evoked, and exhibited short time-to-peak (< 0.5 s), low overshoot (< 10%), low steady-state error (< 4%), and low steady-state ripple (< 12%), with rapid convergence of stimulation parameters. For periodic desired forces, the algorithm was able to quickly converge and experimental trials showed low amplitude error (mean error < 10% of maximum force), and short time delay (< 250 ms)

Doctor of Philosophy

dissertationParalysis due to spinal cord injury or stroke can leave a person with intact peripheral nerves and muscles, but deficient volitional motor control, thereby reducing their health and quality of life. Functional neuromuscular stimulation (FNS) has been widely studied and employed in clinical devices to aid and restore lost or deficient motor function. Strong, selective, and fatigue-resistant muscle forces can be evoked by asynchronously stimulating small independent groups of motor neurons via multiple intrafascicular electrodes on an implanted Utah slanted electrode array (USEA). Determining the parameters of asynchronous intrafascicular multi-electrode stimulation (aIFMS), i.e., the per-electrode stimulus intensities and the interelectrode stimulus phasing, to evoke precise muscle force or joint motion presents unique challenges because this system has multiple-inputs, the n independently stimulated electrodes, but only one measurable output, the evoked endpoint isometric force or joint position. This dissertation presents three studies towards developing robust real-time control of aIFMS. The first study developed an adaptive feedforward algorithm for selecting aIFMS per-electrode stimulus intensities and interelectrode stimulus phasing to evoke a variety of isometric ankle plantar-flexion force trajectories. In simulation and experiments, desired step, sinusoidal, and more-complex time-varying isometric forces were successfully evoked. The second study developed a closed-loop feedback control method for determining aIFMS per-electrode stimulus intensities to evoke precise single-muscle isometric ankle plantar-flexion force trajectories, in real-time. Using a proportional closed-loop force-feedback controller, desired step, sinusoid, and more complex time-varying forces were evoked with good response characteristics, even in the presence of nonlinear system dynamics, such as muscle fatigue. The third study adapted and extended the closed-loop feedback controller to the more demanding task of controlling joint position in the presence of opposing joint torques. A proportional-plus-velocity-plus-integral (PIV) joint-angle feedback controller evoked and held desired steps in position with responses th a t were stable, consistent, and robust to disturbances. The controller evoked smooth ramp-up (concentric) and ramp-down (eccentric) motion, as well as precise slow moving sinusoidal motion. The control methods developed in this dissertation provide a foundation for new lower-limb FNS-based neuroprostheses that can generate sustained and coordinated muscle forces and joint motions that will be desired by paralyzed individuals on a daily basis. proportional-plus-velocity-plus-integral (PIV) joint-angle feedback controller evoked and held desired steps in position with responses th a t were stable, consistent, and robust to disturbances. The controller evoked smooth ramp-up (concentric) and ramp-down (eccentric) motion, as well as precise slow moving sinusoidal motion. The control methods developed in this dissertation provide a foundation for new lower-limb FNS-based neuroprostheses that can generate sustained and coordinated muscle forces and joint motions that will be desired by paralyzed individuals on a daily basis

Doctor of Philosophy

dissertationParalysis can be ameliorated through functional electrical stimulation (FES) of the intact peripheral nerves. The Utah Slanted Electrode Array (USEA) can improve FES systems by providing selective access to many independent motor unit populations.This dissertation includes three studies that expand the role of USEAs in FES applications. The fi rst study leverages the selectivity of the USEA to independently activate the hamstring muscles. Because the di fferent biarticular hamstring muscles can either ex or extend the limb (at the knee or hip), the ability to selectively activate each one independently is required to evoke functional movements such as stance and gait. USEAs implanted in the muscular branch of the sciatic nerve were able to selectively activate each muscle of the hamstring group. Activation of these muscles was graded with increasing stimulus strength, and provided ample dynamic range to allow for fine control of muscle force. The second study demonstrates the ability of the USEA to selectively block neural activity. Upper motor neuron damage can cause hyperre exia and spasticity as well as paralysis. By delivering high-frequency sinusoids through electrodes of the USEA, ber subsets in a nerve were blocked while allowing the remainder of the nerve to function normally. Sinusoids delivered through different electrodes allowed for deactivation of di fferent muscles. The ability to selectively interrupt activity in fiber subpopulations within a nerve will provide new therapeutic options for the positive symptoms of upper motor neuron damage. The fi nal study addresses the practical difficulty of choosing the appropriate stimulus parameters to evoke functional movements. In a USEA-based FES system, the electrodes and stimulus parameters that evoke the desired responses must be identifi ed empirically. USEAs were implanted into three diff erent hind limb nerves, and the response evoked by each electrode was measured noninvasively using 3-D endpoint force. Each electrode was classifi ed as evoking limb flexion or limb extension, and a range of stimulus intensities was identifi ed that evoked a graded force response. Excitation overlap between selected electrode pairs was quantifi ed using the refractory technique. This method will allow for electrode and stimulus parameter selection for use in an FES system using minimal, noninvasive instrumentation

Defining brain–machine interface applications by matching interface performance with device requirements

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications. © 2007 Elsevier B.V. All rights reserved

Neuromorphic hardware for somatosensory neuroprostheses

In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies

An implantable micro-system for neural prosthesis control and sensory feedback restoration in amputees

In this work, the prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of two Integrated Circuits (ICs): a standard CMOS device for neural recording and a High Voltage (HV) CMOS device for neural stimulation. The integrated circuits have been realized in two different 0.35μm CMOS processes available fromAustriaMicroSystem(AMS). The recoding IC incorporates 8 channels each including the analog front-end and the A/D conversion based on a sigma delta architecture. It has a total area of 16.8mm2 and exhibits an overall power consumption of 27.2mW. The neural stimulation IC is able to provide biphasic current pulses to stimulate 8 electrodes independently. A voltage booster generates a 17V voltage supply in order to guarantee the programmed stimulation current even in case of high impedances at the electrode-tissue interface in the order of tens of k­. The stimulation patterns, generated by a 5-bit current DAC, are programmable in terms of amplitude, frequency and pulse width. Due to the huge capacitors of the implemented voltage boosters, the stimulation IC has a wider area of 18.6mm2. In addition, a maximum power consumption of 29mW was measured. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μVrms , and to selectively elicit the tibial and plantarmuscles using different active sites of the electrode. In order to get a completely implantable interface, a biocompatible and biostable package was designed. It hosts the developed ICs with the minimal electronics required for their proper operation. The package consists of an alumina tube closed at both extremities by two ceramic caps hermetically sealed on it. Moreover, the two caps serve as substrate for the hermetic feedthroughs to enable the device powering and data exchange with the external digital controller implemented on a Field-Programmable Gate Array (FPGA) board. The package has an outer diameter of 7mm and a total length of 26mm. In addition, a humidity and temperature sensor was also included inside the package to allow future hermeticity and life-time estimation tests. Moreover, a wireless, wearable and non-invasive EEG recording system is proposed in order to improve the control over the artificial limb,by integrating the neural signals recorded from the PNS with those directly acquired from the brain. To first investigate the system requirements, a Component-Off-The-Shelf (COTS) device was designed. It includes a low-power 8- channel acquisition module and a Bluetooth (BT) transceiver to transmit the acquired data to a remote platform. It was designed with the aimof creating a cheap and user-friendly system that can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates and with a maximum bandwidth of 524Hz. It was realized on a 19cm2 custom PCB with a maximum power consumption of 270mW

ECoG correlates of visuomotor transformation, neural plasticity, and application to a force-based brain computer interface

Electrocorticography: ECoG) has gained increased notoriety over the past decade as a possible recording modality for Brain-Computer Interface: BCI) applications that offers a balance of minimal invasiveness to the patient in addition to robust spectral information over time. More recently, the scale of ECoG devices has begun to shrink to the order of micrometer diameter contacts and millimeter spacings with the intent of extracting more independent signals for BCI control within less cortical real-estate. However, most control signals to date, whether within the field of ECoG or any of the more seasoned recording techniques, have translated their control signals to kinematic control parameters: i.e. position or velocity of an object) which may not be practical for certain BCI applications such as functional neuromuscular stimulation: FNS). Thus, the purpose of this dissertation was to present a novel application of ECoG signals to a force-based control algorithm and address its feasibility for such a BCI system. Micro-ECoG arrays constructed from thin-film polyimide were implanted epidurally over areas spanning premotor, primary motor, and parietal cortical areas of two monkeys: three hemispheres, three arrays). Monkeys first learned to perform a classic center-out task using a brain signal-to-velocity mapping for control of a computer cursor. The BCI algorithm utilized day-to-day adaptation of the decoding model to match the task intention of the monkeys with no need for pre-screeening of movement-related ECoG signals. Using this strategy, subjects showed notable 2-D task profiency and increased task-related modulation of ECoG features within five training sessions. After fixing the last model trained for velocity control of the cursor, the monkeys then utilized this decoding model to control the acceleration of the cursor in the same center-out task. Cursor movement profiles under this mapping paralleled those demonstrated using velocity control, and neural control signal profiles revealed the monkeys actively accelerated and decelerated the cursor within a limited time window: 1-1.5 seconds). The fixed BCI decoding model was recast once again to control the force on a virtual cursor in a novel mass-grab task. This task required targets not only to reach to peripheral targets but also account for an additional virtual mass as they grabbed each target and moved it to a second target location in the presence of the external force of gravity. Examination of the ensemble control signals showed neural adaptation to variations in the perceived mass of the target as well as the presence or absence of gravity. Finally, short rest periods were interleaved within blocks of each task type to elucidate differences between active BCI intention and rest. Using a post-hoc state-decoder model, periods of active BCI task control could be distinguished from periods of rest with a very high degree of accuracy: ~99%). Taken together, the results from these experiments present a first step toward the design of a dynamics-based BCI system suitable for FNS applications as well as a framework for implementation of an asyncrhonous ECoG BCI

Heterogeneous recognition of bioacoustic signals for human-machine interfaces

Human-machine interfaces (HMI) provide a communication pathway between man and machine. Not only do they augment existing pathways, they can substitute or even bypass these pathways where functional motor loss prevents the use of standard interfaces. This is especially important for individuals who rely on assistive technology in their everyday life. By utilising bioacoustic activity, it can lead to an assistive HMI concept which is unobtrusive, minimally disruptive and cosmetically appealing to the user. However, due to the complexity of the signals it remains relatively underexplored in the HMI field. This thesis investigates extracting and decoding volition from bioacoustic activity with the aim of generating real-time commands. The developed framework is a systemisation of various processing blocks enabling the mapping of continuous signals into M discrete classes. Class independent extraction efficiently detects and segments the continuous signals while class-specific extraction exemplifies each pattern set using a novel template creation process stable to permutations of the data set. These templates are utilised by a generalised single channel discrimination model, whereby each signal is template aligned prior to classification. The real-time decoding subsystem uses a multichannel heterogeneous ensemble architecture which fuses the output from a diverse set of these individual discrimination models. This enhances the classification performance by elevating both the sensitivity and specificity, with the increased specificity due to a natural rejection capacity based on a non-parametric majority vote. Such a strategy is useful when analysing signals which have diverse characteristics, false positives are prevalent and have strong consequences, and when there is limited training data available. The framework has been developed with generality in mind with wide applicability to a broad spectrum of biosignals. The processing system has been demonstrated on real-time decoding of tongue-movement ear pressure signals using both single and dual channel setups. This has included in-depth evaluation of these methods in both offline and online scenarios. During online evaluation, a stimulus based test methodology was devised, while representative interference was used to contaminate the decoding process in a relevant and real fashion. The results of this research provide a strong case for the utility of such techniques in real world applications of human-machine communication using impulsive bioacoustic signals and biosignals in general

VLSI Circuits for Bidirectional Neural Interfaces

Medical devices that deliver electrical stimulation to neural tissue are important clinical tools that can augment or replace pharmacological therapies. The success of such devices has led to an explosion of interest in the field, termed neuromodulation, with a diverse set of disorders being targeted for device-based treatment. Nevertheless, a large degree of uncertainty surrounds how and why these devices are effective. This uncertainty limits the ability to optimize therapy and gives rise to deleterious side effects. An emerging approach to improve neuromodulation efficacy and to better understand its mechanisms is to record bioelectric activity during stimulation. Understanding how stimulation affects electrophysiology can provide insights into disease, and also provides a feedback signal to autonomously tune stimulation parameters to improve efficacy or decrease side-effects. The aims of this work were taken up to advance the state-of-the-art in neuro-interface technology to enable closed-loop neuromodulation therapies. Long term monitoring of neuronal activity in awake and behaving subjects can provide critical insights into brain dynamics that can inform system-level design of closed-loop neuromodulation systems. Thus, first we designed a system that wirelessly telemetered electrocorticography signals from awake-behaving rats. We hypothesized that such a system could be useful for detecting sporadic but clinically relevant electrophysiological events. In an 18-hour, overnight recording, seizure activity was detected in a pre-clinical rodent model of global ischemic brain injury. We subsequently turned to the design of neurostimulation circuits. Three critical features of neurostimulation devices are safety, programmability, and specificity. We conceived and implemented a neurostimulator architecture that utilizes a compact on-chip circuit for charge balancing (safety), digital-to-analog converter calibration (programmability) and current steering (specificity). Charge balancing accuracy was measured at better than 0.3%, the digital-to-analog converters achieved 8-bit resolution, and physiological effects of current steering stimulation were demonstrated in an anesthetized rat. Lastly, to implement a bidirectional neural interface, both the recording and stimulation circuits were fabricated on a single chip. In doing so, we implemented a low noise, ultra-low power recording front end with a high dynamic range. The recording circuits achieved a signal-to-noise ratio of 58 dB and a spurious-free dynamic range of better than 70 dB, while consuming 5.5 μW per channel. We demonstrated bidirectional operation of the chip by recording cardiac modulation induced through vagus nerve stimulation, and demonstrated closed-loop control of cardiac rhythm