An Implantable ENG Detector with In-System Velocity Selective Recording (VSR) Capability

Detection and classification of electroneurogram (ENG) signals in the peripheral nervous system can be achieved by velocity selective recording (VSR) using multi-electrode arrays. This paper describes an implantable VSR-based ENG recording system representing a significant development in the field since it is the first system of its type that can record naturally evoked ENG and be interfaced wirelessly using a low data rate transcutaneous link. The system consists of two CMOS ASICs one of which is placed close to the multi-electrode cuff array (MEC), whilst the other is mounted close to the wireless link. The digital ASIC provides the signal processing required to detect selectively ENG signals based on velocity. The design makes use of an original architecture that is suitable for implantation and reduces the required data rate for transmission to units placed outside the body. Complete measured electrical data from samples of the ASICs are presented that show that the system has the capability to record signals of amplitude as low as 0.5 μV, which is adequate for the recording of naturally evoked ENG. In addition, measurements of electrically evoked ENG from the explanted sciatic nerves of Xenopus Laevis frogs are presented

Restoring Sensation of Gravitoinertial Acceleration through Prosthetic Stimulation of the Utricle and Saccule

Individuals with bilateral vestibular hypofunction suffer reduced quality of life due to loss of postural and ocular reflexes essential to maintaining balance and visual acuity during head movements. Vestibular stimulation has demonstrated success in restoring sensation of angular head rotations using electrical stimulation of the semi-circular canals (SCCs). Efforts toward utricle and saccule stimulation to restore sensation of gravitoinertial acceleration have been limited due to the complexity of the otolith end organs and otolith-ocular reflexes (OORs). Four key pieces of technology were developed to extend prosthetic stimulation to the utricle and saccule: a low-noise scleral coil system to record binocular 3D eye movements; a motion platform control system for automated presentation of rotational and translational stimuli; custom electrode arrays with fifty contacts targeting the SCCs, utricle and saccule; and a general-purpose neuroelectronic stimulator for vestibular and other neuromodulation applications. Using these new technologies, OORs were first characterized in six chinchillas to establish OOR norms during translations and static tilts. Results led to creation of a model that infers the axis of head tilt from measured binocular eye movements and thereby provides a context and means to assess the selectivity of prosthetic utricle and saccule stimulation. The model confirms the expectation that excitation of the left utricle and saccule primarily encodes tilts that bring the left ear down. Three of the chinchillas were implanted with electrode arrays in the left ear. Step changes in pulse rate were delivered to utricle and saccule electrodes near the maculae while measuring 3D binocular eye movements with the animal stationary in darkness. These stimuli elicited sustained ocular counter-roll responses that increased in magnitude as pulse rate or amplitude increased. Bipolar stimulation via neighboring electrodes elicited slow-rising or delayed onset of ocular counter-rolls (consistent with normal translational OOR low-pass filter behavior). Two chinchillas showed different direction of electrically-evoked ocular counter-roll between utricle versus saccule stimulation. Only near-neighbor bipolar electrode combinations elicited eye responses compensatory for tilts other than the ‘usual’ left ear down, suggesting the need for distributing multiple bipolar electrode pairs across the maculae to achieve selective stimulation and restore 3D sensation of gravitoinertial acceleration

Restoring Fine Motor Skills through Neural Interface Technology.

Loss of motor function in the upper-limb, whether through paralysis or through loss of the limb itself, is a profound disability which affects a large population worldwide. Lifelike, fully-articulated prosthetic hands exist and are commercially available; however, there is currently no satisfactory method of controlling all of the available degrees of freedom. In order to generate better control signals for this technology, and help restore normal movement, it is necessary to interface directly with the nervous system. This thesis is intended to address several of the limitations of current neural interfaces and enable the long-term extraction of control signals for fine movements of the hand and fingers. The first study addresses the problems of low signal amplitudes and short implant lifetimes in peripheral nerve interfaces. In two rhesus macaques, we demonstrate the successful implantation of regenerative peripheral nerve interfaces (RPNI), which allowed us to record high amplitude, functionally-selective signals from peripheral nerves up to 20 months post-implantation. These signals could be accurately decoded into intended movement, and used to enable monkeys to control a virtual hand prosthesis. The second study presents a novel experimental paradigm for intracortical neural interfaces, which enables detailed investigation of fine motor information contained in primary motor cortex. We used this paradigm to demonstrate accurate decoding of continuous fingertip position and enable a monkey to control a virtual hand in closed-loop. This is the first demonstration of volitional control of fine motor skill enabled by a cortical neural interface. The final study presents the design and testing of a wireless implantable neural recording system. By extracting signal power in a single, configurable frequency band onboard the device, this system achieves low power consumption while maintaining decode performance, and is applicable to cortical, peripheral, and myoelectric signals. Taken together, these results represent a significant step towards clinical reality for neural interfaces, and towards restoration of full and dexterous movement for people with severe disabilities.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120648/1/irwinz_1.pd

Digital neuromorphic auditory systems

This dissertation presents several digital neuromorphic auditory systems. Neuromorphic systems are capable of running in real-time at a smaller computing cost and consume lower power than on widely available general computers. These auditory systems are considered neuromorphic as they are modelled after computational models of the mammalian auditory pathway and are capable of running on digital hardware, or more specifically on a field-programmable gate array (FPGA). The models introduced are categorised into three parts: a cochlear model, an auditory pitch model, and a functional primary auditory cortical (A1) model. The cochlear model is the primary interface of an input sound signal and transmits the 2D time-frequency representation of the sound to the pitch models as well as to the A1 model. In the pitch model, pitch information is extracted from the sound signal in the form of a fundamental frequency. From the A1 model, timbre information in the form of time-frequency envelope information of the sound signal is extracted. Since the computational auditory models mentioned above are required to be implemented on FPGAs that possess fewer computational resources than general-purpose computers, the algorithms in the models are optimised so that they fit on a single FPGA. The optimisation includes using simplified hardware-implementable signal processing algorithms. Computational resource information of each model on FPGA is extracted to understand the minimum computational resources required to run each model. This information includes the quantity of logic modules, register quantity utilised, and power consumption. Similarity comparisons are also made between the output responses of the computational auditory models on software and hardware using pure tones, chirp signals, frequency-modulated signal, moving ripple signals, and musical signals as input. The limitation of the responses of the models to musical signals at multiple intensity levels is also presented along with the use of an automatic gain control algorithm to alleviate such limitations. With real-world musical signals as their inputs, the responses of the models are also tested using classifiers – the response of the auditory pitch model is used for the classification of monophonic musical notes, and the response of the A1 model is used for the classification of musical instruments with their respective monophonic signals. Classification accuracy results are shown for model output responses on both software and hardware. With the hardware implementable auditory pitch model, the classification score stands at 100% accuracy for musical notes from the 4th and 5th octaves containing 24 classes of notes. With the hardware implementation auditory timbre model, the classification score is 92% accuracy for 12 classes musical instruments. Also presented is the difference in memory requirements of the model output responses on both software and hardware – pitch and timbre responses used for the classification exercises use 24 and 2 times less memory space for hardware than software

An Implantable Peripheral Nerve Recording and Stimulation System for Experiments on Freely Moving Animal Subjects

A new study with rat sciatic nerve model for peripheral nerve interfacing is presented using a fully-implanted inductively-powered recording and stimulation system in a wirelessly-powered standard homecage that allows animal subjects move freely within the homecage. The Wireless Implantable Neural Recording and Stimulation (WINeRS) system offers 32-channel peripheral nerve recording and 4-channel current-controlled stimulation capabilities in a 3 × 1.5 × 0.5 cm3 package. A bi-directional data link is established by on-off keying pulse-position modulation (OOK-PPM) in near field for narrow-band downlink and 433 MHz OOK for wideband uplink. An external wideband receiver is designed by adopting a commercial software defined radio (SDR) for a robust wideband data acquisition on a PC. The WINeRS-8 prototypes in two forms of battery-powered headstage and wirelessly-powered implant are validated in vivo, and compared with a commercial system. In the animal study, evoked compound action potentials were recorded to verify the stimulation and recording capabilities of the WINeRS-8 system with 32-ch penetrating and 4-ch cuff electrodes on the sciatic nerve of awake freely-behaving rats. Compared to the conventional battery-powered system, WINeRS can be used in closed-loop recording and stimulation experiments over extended periods without adding the burden of carrying batteries on the animal subject or interrupting the experiment

Development of a Micro Recording Probe for Measurements of Neuronal Activity in Freely Moving Animals

To discover general principles of biological sensorimotor control, insects have become remarkably successful model systems. In contrast to highly complex mammals, the functional organization of the insect nervous system in combination with a well-defined behavioural repertoire turned out to provide ideal conditions for quantitative studies into the neural control of behaviour. In addition, the search for biologically inspired control algorithms has further accelerated research into the neuronal mechanisms underlying flight and gaze stabilization, especially in blowflies. However, recording the neuronal activity in freely behaving insects, in particular in comparatively small insects such as blowflies, still imposes a major technical challenge. To date, electrophysiological recordings in unrestrained flies have never been achieved. This thesis describes the design and testing of a micro recording probe to be used for monitoring extracellular electrical activity in the nervous system of freely moving blowflies. In principle, this probe could also be used to study the neuronal control of behaviour in any other animal species the size of which is bigger than that of a blowfly. The nature of neuronal signals and the objective to record neuronal activity from behaving blowflies puts massive constraints on the specifications of the probe. I designed a differential amplifier with high gain, high linearity, low noise, and low power consumption. To fit the probe in the blowfly‟s head capsule and in direct contact with the animal‟s brain, the amplifier is on an unpackaged die. The neuronal signals are in the order of a few 100s of μV in amplitude. To be able to digitize such small signals >1000 times amplification is desirable. The small signal amplitudes also necessitate minimization of circuit noise. Linearity is necessary to prevent distortion of signal shape. Since connecting wires would impede movement of the animal, the probe would need to be powered by batteries. Therefore, low power is needed for two reasons: (i) to increase battery life, and therefore recording time, and (ii) because heat caused by power expenditure may damage the blowfly‟s brain or change its behaviour. To reduce power consumption I used CMOS transistors biased in the subthreshold region and a 2.2 V low power supply. The amplifier was characterized after fabrication by means of measuring its frequency response, linearity, and noise. I also recorded signals from a blowfly's brain and compared the performance of my recording probe with the performance of a high specification commercial amplifier in the time and frequency domains

High speed event-based visual processing in the presence of noise

Standard machine vision approaches are challenged in applications where large amounts of noisy temporal data must be processed in real-time. This work aims to develop neuromorphic event-based processing systems for such challenging, high-noise environments. The novel event-based application-focused algorithms developed are primarily designed for implementation in digital neuromorphic hardware with a focus on noise robustness, ease of implementation, operationally useful ancillary signals and processing speed in embedded systems