A multi-channel wireless implantable neural recording (WINeR) system for electrophysiology and behavioral neuroscience research applications was proposed. The system is composed of two units: a system-on-a-chip (SoC) transmitter (Tx) unit and a receiver (Rx) unit. In the Tx unit, the outputs are combined with marker signals and modulated into pulse widths after the neural signals are amplified and filtered by an array of low-noise amplifiers (LNA). The next step involves time-division multiplexing (TDM) of pulse-width modulation (PWM) signals. The TDM-PWM signal drives RF transmitter block and is transmitted by an antenna. To satisfy the needs of neuroscientists during animal experiments, the proposed WINeR system provides long-term recording with inductive powering and stimulus-artifact rejection for closed-loop operations, which requires simultaneous stimulation and recording. The Rx is another critical unit for wireless-link communication. To increase the area of wireless coverage, multiple antennas are used for the Rx. In addition, the automatic frequency-tracking method is used to track free-running Tx frequencies, and a smart time-to-digital conversion method is used to reduce noise and interference. A high-throughput computer interface and software are also developed to continuously receive and store neural data. The WINeR system is a potential tool for neuroscientists due to several advantages, such as a reliable wireless link with large coverage and no blind spots, low power consumption, an unlimited power source, and a stimulation function.

Neural recording systems have attracted an increasing amount of attention in recent years, and researchers have put major efforts into designing and developing devices that can record and monitor neural activity. Understanding the functionality of neurons can be used to develop neuroprosthetics for restoring damages in the nervous system. An analog front-end block is one of the main components in such systems, by which the neuron signals are amplified and processed for further analysis. In this work, our goal is to design and implement a field-programmable 16-channel analog front-end block, where its programmability is used to deal with process variation in the chip. Each channel consists of a two-stage amplifier as well as a band-pass filter with digitally tunable low corner frequency. The 16 recording channels are designed using four different architectures. The first group of recording channels employs one low-noise amplifier (LNA) as the first-stage amplifier and a fully differential amplifier for the second stage along with an NMOS transistor in the feedback loop. In the second group of architectures, we use an LNA as the first stage and a single-ended amplifier for implementing the second stage. Groups three and four have the same design as groups one and two; however the NMOS transistor in the feedback loop is replaced by two PMOS transistors. In our design, the circuits are optimized for low noise and low power consumption. Simulations result in input-referred noise of 6.9 ??Vrms over 0.1 Hz to 1 GHz. Our experiments show the recording channel has a gain of 77.5 dB. The chip is fabricated in AMS 0.35 ??m CMOS technology for a total die area of 3 mm??3 mm and consumes 2.7 mW power from a 3.3 V supply. Moreover, the chip is tested on a PCB board that can be employed for in-vivo recording.

Injuries involving the nervous system are among the most devastating and life altering of all neurological disorders. The resulting loss of sensation and voluntary muscle control represent a drastic change in the individuals lifestyle and independence. Spinal cord injury affects over two hundred thousand people within the United States alone. While there have been many attempts to develop neural interfaces that can be used as part of a prosthetic device to improve the quality of life of such patients and contribute to the reduction of ongoing health care costs, the design of such a device has proved elusive. Direct access to the spinal cord requires potentially life threatening surgery during which the dura, the protective covering surrounding the cord, must be opened with a resulting high risk of infection. For this reason research has been focussed on the stimulation of and recording from the peripheral nerves in an attempt to restore the functionality that has been lost through spinal cord injury. This thesis is concerned with the current status and limitations of peripheral nerve interfaces that are designed for recording electrical signals directly from the nervous system using a technique called velocity selective recording. This technique exploits the relationship between axonal diameter, which is linked via anatomy to function, and the speed with which the axon conducts excitation. New techniques are developed that improve current methods for identifying and simulating neural signals and power efficient implementations of these methods are presented in modern microelectronic platforms. Results are presented from pioneering experiments in rat and pig that for the first time demonstrate the recording and analysis of the physiological electroneurogram using velocity based methods. New methods are developed that enable the extraction of neuronal firing rates and thus the extraction of the information encoded within the nervous system.

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abstract: Intracellular voltage recordings from single neurons in vitro and in vivo have been fundamental to our understanding of neuronal function. Conventional electrodes and associated positioning systems for intracellular recording in vivo are large and bulky, which has largely restricted their use to single-channel recording from anesthetized animals. Further, intracellular recordings are very cumbersome, requiring a high degree of skill not readily achieved in a typical laboratory. This dissertation presents a robotic, head-mountable, MEMS (Micro-Electro-Mechanical Systems) based intracellular recording system to overcome the above limitations associated with form-factor, scalability and highly skilled and tedious manual operations required for intracellular recordings. This system combines three distinct technologies: 1) novel microscale, polycrystalline silicon-based electrode for intracellular recording, 2) electrothermal microactuators for precise microscale navigation of the electrode and 3) closed-loop control algorithm for autonomous movement and positioning of electrode inside single neurons. First, two distinct designs of polysilicon-based microscale electrodes were fabricated and tested for intracellular recordings. In the first approach, tips of polysilicon microelectrodes were milled to nanoscale dimensions (<300 nm) using focused ion beam (FIB) to develop polysilicon nanoelectrodes. Polysilicon nanoelectrodes recorded >1.5 mV amplitude, positive-going action potentials and synaptic potentials from neurons in the abdominal ganglion of Aplysia Californica. In the second approach, polysilicon microelectrodes were integrated with miniaturized glass micropipettes filled with electrolyte to fabricate glass-polysilicon microelectrodes. These electrodes consistently recorded high fidelity intracellular potentials from neurons in the abdominal ganglion of Aplysia Californica (Resting Potentials < -35 mV, Action Potentials > 60 mV) as well as the rat motor cortex (Resting Potentials < -50 mV). Next, glass-polysilicon microelectrodes were coupled with microscale electrothermal actuators and controller for autonomous intracellular recordings from single neurons in the abdominal ganglion. Consistent resting potentials (< -35 mV) and action potentials (> 60 mV) were recorded after each successful penetration attempt with the controller and microactuated glass-polysilicon microelectrodes. The success rate of penetration and quality of recordings achieved using electrothermal microactuators were comparable to that of conventional positioning systems. Finally, the feasibility of this miniaturized system to obtain intracellular recordings from single neurons in the motor cortex of rats in vivo is also demonstrated. The MEMS-based system offers significant advantages: 1) reduction in overall size for potential use in behaving animals, 2) scalable approach to potentially realize multi-channel recordings and 3) a viable method to fully automate measurement of intracellular recordings. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2018

<p>Deep brain stimulation (DBS) is an established surgical therapy for movement disorders. The mechanisms of action of DBS remain unclear, and selection of stimulation parameters is a clinical challenge and can result in sub-optimal outcomes. Closed-loop DBS systems would use a feedback control signal for automatic adjustment of DBS parameters and improved therapeutic effectiveness. We hypothesized that evoked compound action potentials (ECAPs), generated by activated neurons in the vicinity of the stimulating electrode, would reveal the type and spatial extent of neural activation, as well as provide signatures of clinical effectiveness. The objective of this dissertation was to record and characterize the ECAP during DBS to determine its suitability as a feedback signal in closed-loop systems. The ECAP was investigated using computer simulation and <italic>in vivo</italic> experiments, including the first preclinical and clinical ECAP recordings made from the same DBS electrode implanted for stimulation. </p><p>First, we developed DBS-ECAP recording instrumentation to reduce the stimulus artifact and enable high fidelity measurements of the ECAP at short latency. <italic>In vitro</italic> and <italic>in vivo</italic> validation experiments demonstrated the capability of the instrumentation to suppress the stimulus artifact, increase amplifier gain, and reduce distortion of short latency ECAP signals.</p><p>Second, we characterized ECAPs measured during thalamic DBS across stimulation parameters in anesthetized cats, and determined the neural origin of the ECAP using pharmacological interventions and a computer-based biophysical model of a thalamic network. This model simulated the ECAP response generated by a population of thalamic neurons, calculated ECAPs similar to experimental recordings, and indicated the relative contribution from different types of neural elements to the composite ECAP. Signal energy of the ECAP increased with DBS amplitude or pulse width, reflecting an increased extent of activation. Shorter latency, primary ECAP phases were generated by direct excitation of neural elements, whereas longer latency, secondary phases were generated by post-synaptic activation.</p><p>Third, intraoperative studies were conducted in human subjects with thalamic DBS for tremor, and the ECAP and tremor responses were measured across stimulation parameters. ECAP recording was technically challenging due to the presence of a wide range of stimulus artifact magnitudes across subjects, and an electrical circuit equivalent model and finite element method model both suggested that glial encapsulation around the DBS electrode increased the artifact size. Nevertheless, high fidelity ECAPs were recorded from acutely and chronically implanted DBS electrodes, and the energy of ECAP phases was correlated with changes in tremor. </p><p>Fourth, we used a computational model to understand how electrode design parameters influenced neural recording. Reducing the diameter or length of recording contacts increased the magnitude of single-unit responses, led to greater spatial sensitivity, and changed the relative contribution from local cells or passing axons. The effect of diameter or contact length varied across phases of population ECAPs, but ECAP signal energy increased with greater contact spacing, due to changes in the spatial sensitivity of the contacts. In addition, the signal increased with glial encapsulation in the peri-electrode space, decreased with local edema, and was unaffected by the physical presence of the highly conductive recording contacts.</p><p>It is feasible to record ECAP signals during DBS, and the correlation between ECAP characteristics and tremor suggests that this signal could be used in closed-loop DBS. This was demonstrated by implementation in simulation of a closed-loop system, in which a proportional-integral-derivative (PID) controller automatically adjusted DBS parameters to obtain a target ECAP energy value, and modified parameters in response to disturbances. The ECAP also provided insight into neural activation during DBS, with the dominant contribution to clinical ECAPs derived from excited cerebellothalamic fibers, suggesting that activation of these fibers is critical for DBS therapy.</p> / Dissertation

abstract: Long-term monitoring of deep brain structures using microelectrode implants is critical for the success of emerging clinical applications including cortical neural prostheses, deep brain stimulation and other neurobiology studies such as progression of disease states, learning and memory, brain mapping etc. However, current microelectrode technologies are not capable enough of reaching those clinical milestones given their inconsistency in performance and reliability in long-term studies. In all the aforementioned applications, it is important to understand the limitations & demands posed by technology as well as biological processes. Recent advances in implantable Micro Electro Mechanical Systems (MEMS) technology have tremendous potential and opens a plethora of opportunities for long term studies which were not possible before. The overall goal of the project is to develop large scale autonomous, movable, micro-scale interfaces which can seek and monitor/stimulate large ensembles of precisely targeted neurons and neuronal networks that can be applied for brain mapping in behaving animals. However, there are serious technical (fabrication) challenges related to packaging and interconnects, examples of which include: lack of current industry standards in chip-scale packaging techniques for silicon chips with movable microstructures, incompatible micro-bonding techniques to elongate current micro-electrode length to reach deep brain structures, inability to achieve hermetic isolation of implantable devices from biological tissue and fluids (i.e. cerebrospinal fluid (CSF), blood, etc.). The specific aims are to: 1) optimize & automate chip scale packaging of MEMS devices with unique requirements not amenable to conventional industry standards with respect to bonding, process temperature and pressure in order to achieve scalability 2) develop a novel micro-bonding technique to extend the length of current polysilicon micro-electrodes to reach and monitor deep brain structures 3) design & develop high throughput packaging mechanism for constructing a dense array of movable microelectrodes. Using a combination of unique micro-bonding technique which involves conductive thermosetting epoxy’s with hermetically sealed support structures and a highly optimized, semi-automated, 90-minute flip-chip packaging process, I have now extended the repertoire of previously reported movable microelectrode arrays to bond conventional stainless steel and Pt/Ir microelectrode arrays of desired lengths to steerable polysilicon shafts. I tested scalable prototypes in rigorous bench top tests including Impedance measurements, accelerated aging and non-destructive testing to assess electrical and mechanical stability of micro-bonds under long-term implantation. I propose a 3D printed packaging method allows a wide variety of electrode configurations to be realized such as a rectangular or circular array configuration or other arbitrary geometries optimal for specific regions of the brain with inter-electrode distance as low as 25 um with an unprecedented capability of seeking and recording/stimulating targeted single neurons in deep brain structures up to 10 mm deep (with 6 μm displacement resolution). The advantage of this computer controlled moveable deep brain electrodes facilitates potential capabilities of moving past glial sheath surrounding microelectrodes to restore neural connection, counter the variabilities in signal amplitudes, and enable simultaneous recording/stimulation at precisely targeted layers of brain. / Dissertation/Thesis / Masters Thesis Bioengineering 2016

abstract: Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface. / Dissertation/Thesis / Ph.D. Bioengineering 2013

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and to elucidate human neurophysiology. The advent of multi-channel micro-electrode arrays has driven the need for electronic store cord neural signals from many neurons. The continuous increase in demand of data from more number of neurons is challenging for the design of an efficient neural recording frontend(NRFE). Power consumption per channel and data rate minimization are two key problems which need to be addressed by next generation of neural recording systems. Area consumption per channel must be low for small implant size. Dynamic range in NRFE can vary with time due to change in electrode-neuron distance or background noise which demands adaptability. In this thesis, techniques to reduce power-per-channel and area-per-channel in a NRFE, via new circuits and architectures, are proposed. An area efficient low power neural LNA is presented in UMC 0.13 μm 1P8M CMOS technology. The amplifier can be biased adaptively from 200 nA to 2 μA , modulating input referred noise from 9.92 μV to 3.9μV . We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. Optimum sizing of the input transistors minimizes the accentuation of the input referred noise of the amplifier. It obviates the need of large input coupling capacitance in the amplifier which saves considerable amount of chip area. In vitro experiments were performed to validate the applicability of the neural LNA in neural recording systems. ADC is another important block in a NRFE. An 8-bit SAR ADC along with the input and reference buffer is implemented in 0.13 μm CMOS technology. The use of ping-pong input sampling is emphasized for multichannel input to alleviate the bandwidth requirement of the input buffer. To reduce the output data rate, the A/D process is only enabled through a proposed activity dependent A/D scheme which ensures that the background noise is not processed. Based on the dynamic range requirement, the ADC resolution is adjusted from 8 to 1 bit at 1 bit step to reduce power consumption linearly. The ADC consumes 8.8 μW from1Vsupply at1MS/s and achieves ENOB of 7.7 bit. The ADC achieves FoM of 42.3 fJ/conversion in 0.13 μm CMOS technology. Power consumption in SARADCs is greatly benefited by CMOS scaling due to its highly digital nature. However the power consumption in the capacitive DAC does not scale as well as the digital logic. In this thesis, two energy-efficient DAC switching techniques, Flip DAC and Quaternary capacitor switching, are proposed to reduce their energy consumption. Using these techniques, the energy consumption in the DAC can be reduced by 37 % and 42.5 % compared to the present state-of-the-art. A novel concept of code-independent energy consumption is introduced and emphasized. It mitigates energy consumption degradation with small input signal dynamic range.

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