1,324 research outputs found

    A neural probe with up to 966 electrodes and up to 384 configurable channels in 0.13 ฮผm SOI CMOS

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    In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13-ฮผm SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 ฮผm wide, 10 mm long, 20 ฮผm thick), achieving a crosstalk of โˆ’64.4 dB. The probe base (5 ร— 9 mm2) implements dual-band recording and a 1

    Active C4 electrodes for local field potential recording applications

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    Extracellular neural recording, with multi-electrode arrays (MEAs), is a powerful method used to study neural function at the network level. However, in a high density array, it can be costly and time consuming to integrate the active circuit with the expensive electrodes. In this paper, we present a 4 mm ร— 4 mm neural recording integrated circuit (IC) chip, utilizing IBM C4 bumps as recording electrodes, which enable a seamless active chip and electrode integration. The IC chip was designed and fabricated in a 0.13 ฮผm BiCMOS process for both in vitro and in vivo applications. It has an input-referred noise of 4.6 ฮผV rms for the bandwidth of 10 Hz to 10 kHz and a power dissipation of 11.25 mW at 2.5 V, or 43.9 ฮผW per input channel. This prototype is scalable for implementing larger number and higher density electrode arrays. To validate the functionality of the chip, electrical testing results and acute in vivo recordings from a rat barrel cortex are presented.R01 NS072385 - NINDS NIH HHS; 1R01 NS072385 - NINDS NIH HH

    Communication channel analysis and real time compressed sensing for high density neural recording devices

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    Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

    Recent Advances in Neural Recording Microsystems

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    The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

    Amplifiers in Biomedical Engineering: A Review from Application Perspectives

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    Continuous monitoring and treatment of various diseases with biomedical technologies and wearable electronics has become significantly important. The healthcare area is an important, evolving field that, among other things, requires electronic and micro-electromechanical technologies. Designed circuits and smart devices can lead to reduced hospitalization time and hospitals equipped with high-quality equipment. Some of these devices can also be implanted inside the body. Recently, various implanted electronic devices for monitoring and diagnosing diseases have been presented. These instruments require communication links through wireless technologies. In the transmitters of these devices, power amplifiers are the most important components and their performance plays important roles. This paper is devoted to collecting and providing a comprehensive review on the various designed implanted amplifiers for advanced biomedical applications. The reported amplifiers vary with respect to the class/type of amplifier, implemented CMOS technology, frequency band, output power, and the overall efficiency of the designs. The purpose of the authors is to provide a general view of the available solutions, and any researcher can obtain suitable circuit designs that can be selected for their problem by reading this survey

    ์†Œํ˜•๋™๋ฌผ์˜ ๋‡Œ์‹ ๊ฒฝ ์ž๊ทน์„ ์œ„ํ•œ ์™„์ „ ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€,2020. 2. ๊น€์„ฑ์ค€.In this study, a fully implantable neural stimulator that is designed to stimulate the brain in the small animal is described. Electrical stimulation of the small animal is applicable to pre-clinical study, and behavior study for neuroscience research, etc. Especially, behavior study of the freely moving animal is useful to observe the modulation of sensory and motor functions by the stimulation. It involves conditioning animal's movement response through directional neural stimulation on the region of interest. The main technique that enables such applications is the development of an implantable neural stimulator. Implantable neural stimulator is used to modulate the behavior of the animal, while it ensures the free movement of the animals. Therefore, stable operation in vivo and device size are important issues in the design of implantable neural stimulators. Conventional neural stimulators for brain stimulation of small animal are comprised of electrodes implanted in the brain and a pulse generation circuit mounted on the back of the animal. The electrical stimulation generated from the circuit is conveyed to the target region by the electrodes wire-connected with the circuit. The devices are powered by a large battery, and controlled by a microcontroller unit. While it represents a simple approach, it is subject to various potential risks including short operation time, infection at the wound, mechanical failure of the device, and animals being hindered to move naturally, etc. A neural stimulator that is miniaturized, fully implantable, low-powered, and capable of wireless communication is required. In this dissertation, a fully implantable stimulator with remote controllability, compact size, and minimal power consumption is suggested for freely moving animal application. The stimulator consists of modular units of surface-type and depth-type arrays for accessing target brain area, package for accommodating the stimulating electronics all of which are assembled after independent fabrication and implantation using customized flat cables and connectors. The electronics in the package contains ZigBee telemetry for low-power wireless communication, inductive link for recharging lithium battery, and an ASIC that generates biphasic pulse for neural stimulation. A dual-mode power-saving scheme with a duty cycling was applied to minimize the power consumption. All modules were packaged using liquid crystal polymer (LCP) to avoid any chemical reaction after implantation. To evaluate the fabricated stimulator, wireless operation test was conducted. Signal-to-Noise Ratio (SNR) of the ZigBee telemetry were measured, and its communication range and data streaming capacity were tested. The amount of power delivered during the charging session depending on the coil distance was measured. After the evaluation of the device functionality, the stimulator was implanted into rats to train the animals to turn to the left (or right) following a directional cue applied to the barrel cortex. Functionality of the device was also demonstrated in a three-dimensional maze structure, by guiding the rats to navigate better in the maze. Finally, several aspects of the fabricated device were discussed further.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์†Œํ˜• ๋™๋ฌผ์˜ ๋‘๋‡Œ๋ฅผ ์ž๊ทนํ•˜๊ธฐ ์œ„ํ•œ ์™„์ „ ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๊ฐ€ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ์†Œํ˜• ๋™๋ฌผ์˜ ์ „๊ธฐ์ž๊ทน์€ ์ „์ž„์ƒ ์—ฐ๊ตฌ, ์‹ ๊ฒฝ๊ณผํ•™ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ ํ–‰๋™์—ฐ๊ตฌ ๋“ฑ์— ํ™œ์šฉ๋œ๋‹ค. ํŠนํžˆ, ์ž์œ ๋กญ๊ฒŒ ์›€์ง์ด๋Š” ๋™๋ฌผ์„ ๋Œ€์ƒ์œผ๋กœ ํ•œ ํ–‰๋™ ์—ฐ๊ตฌ๋Š” ์ž๊ทน์— ์˜ํ•œ ๊ฐ๊ฐ ๋ฐ ์šด๋™ ๊ธฐ๋Šฅ์˜ ์กฐ์ ˆ์„ ๊ด€์ฐฐํ•˜๋Š” ๋ฐ ์œ ์šฉํ•˜๊ฒŒ ํ™œ์šฉ๋œ๋‹ค. ํ–‰๋™ ์—ฐ๊ตฌ๋Š” ๋‘๋‡Œ์˜ ํŠน์ • ๊ด€์‹ฌ ์˜์—ญ์„ ์ง์ ‘์ ์œผ๋กœ ์ž๊ทนํ•˜์—ฌ ๋™๋ฌผ์˜ ํ–‰๋™๋ฐ˜์‘์„ ์กฐ๊ฑดํ™”ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ˆ˜ํ–‰๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ ์šฉ์„ ๊ฐ€๋Šฅ์ผ€ ํ•˜๋Š” ํ•ต์‹ฌ๊ธฐ์ˆ ์€ ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ์˜ ๊ฐœ๋ฐœ์ด๋‹ค. ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋Š” ๋™๋ฌผ์˜ ์›€์ง์ž„์„ ๋ฐฉํ•ดํ•˜์ง€ ์•Š์œผ๋ฉด์„œ๋„ ๊ทธ ํ–‰๋™์„ ์กฐ์ ˆํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋™๋ฌผ ๋‚ด์—์„œ์˜ ์•ˆ์ •์ ์ธ ๋™์ž‘๊ณผ ์žฅ์น˜์˜ ํฌ๊ธฐ๊ฐ€ ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋ฅผ ์„ค๊ณ„ํ•จ์— ์žˆ์–ด ์ค‘์š”ํ•œ ๋ฌธ์ œ์ด๋‹ค. ๊ธฐ์กด์˜ ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋Š” ๋‘๋‡Œ์— ์ด์‹๋˜๋Š” ์ „๊ทน ๋ถ€๋ถ„๊ณผ, ๋™๋ฌผ์˜ ๋“ฑ ๋ถ€๋ถ„์— ์œ„์น˜ํ•œ ํšŒ๋กœ๋ถ€๋ถ„์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ํšŒ๋กœ์—์„œ ์ƒ์‚ฐ๋œ ์ „๊ธฐ์ž๊ทน์€ ํšŒ๋กœ์™€ ์ „์„ ์œผ๋กœ ์—ฐ๊ฒฐ๋œ ์ „๊ทน์„ ํ†ตํ•ด ๋ชฉํ‘œ ์ง€์ ์œผ๋กœ ์ „๋‹ฌ๋œ๋‹ค. ์žฅ์น˜๋Š” ๋ฐฐํ„ฐ๋ฆฌ์— ์˜ํ•ด ๊ตฌ๋™๋˜๋ฉฐ, ๋‚ด์žฅ๋œ ๋งˆ์ดํฌ๋กœ ์ปจํŠธ๋กค๋Ÿฌ์— ์˜ํ•ด ์ œ์–ด๋œ๋‹ค. ์ด๋Š” ์‰ฝ๊ณ  ๊ฐ„๋‹จํ•œ ์ ‘๊ทผ๋ฐฉ์‹์ด์ง€๋งŒ, ์งง์€ ๋™์ž‘์‹œ๊ฐ„, ์ด์‹๋ถ€์œ„์˜ ๊ฐ์—ผ์ด๋‚˜ ์žฅ์น˜์˜ ๊ธฐ๊ณ„์  ๊ฒฐํ•จ, ๊ทธ๋ฆฌ๊ณ  ๋™๋ฌผ์˜ ์ž์—ฐ์Šค๋Ÿฌ์šด ์›€์ง์ž„ ๋ฐฉํ•ด ๋“ฑ ์—ฌ๋Ÿฌ ๋ฌธ์ œ์ ์„ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์˜ ๊ฐœ์„ ์„ ์œ„ํ•ด ๋ฌด์„ ํ†ต์‹ ์ด ๊ฐ€๋Šฅํ•˜๊ณ , ์ €์ „๋ ฅ, ์†Œํ˜•ํ™”๋œ ์™„์ „ ์ด์‹ํ˜• ์‹ ๊ฒฝ์ž๊ทน๊ธฐ์˜ ์„ค๊ณ„๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž์œ ๋กญ๊ฒŒ ์›€์ง์ด๋Š” ๋™๋ฌผ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์›๊ฒฉ ์ œ์–ด๊ฐ€ ๊ฐ€๋Šฅํ•˜๋ฉฐ, ํฌ๊ธฐ๊ฐ€ ์ž‘๊ณ , ์†Œ๋ชจ์ „๋ ฅ์ด ์ตœ์†Œํ™”๋œ ์™„์ „์ด์‹ํ˜• ์ž๊ทน๊ธฐ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์„ค๊ณ„๋œ ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋Š” ๋ชฉํ‘œ๋กœ ํ•˜๋Š” ๋‘๋‡Œ ์˜์—ญ์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋Š” ํ‘œ๋ฉดํ˜• ์ „๊ทน๊ณผ ํƒ์นจํ˜• ์ „๊ทน, ๊ทธ๋ฆฌ๊ณ  ์ž๊ทน ํŽ„์Šค ์ƒ์„ฑ ํšŒ๋กœ๋ฅผ ํฌํ•จํ•˜๋Š” ํŒจํ‚ค์ง€ ๋“ฑ์˜ ๋ชจ๋“ˆ๋“ค๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ, ๊ฐ๊ฐ์˜ ๋ชจ๋“ˆ์€ ๋…๋ฆฝ์ ์œผ๋กœ ์ œ์ž‘๋˜์–ด ๋™๋ฌผ์— ์ด์‹๋œ ๋’ค ์ผ€์ด๋ธ”๊ณผ ์ปค๋„ฅํ„ฐ๋กœ ์—ฐ๊ฒฐ๋œ๋‹ค. ํŒจํ‚ค์ง€ ๋‚ด๋ถ€์˜ ํšŒ๋กœ๋Š” ์ €์ „๋ ฅ ๋ฌด์„ ํ†ต์‹ ์„ ์œ„ํ•œ ์ง€๊ทธ๋น„ ํŠธ๋žœ์‹œ๋ฒ„, ๋ฆฌํŠฌ ๋ฐฐํ„ฐ๋ฆฌ์˜ ์žฌ์ถฉ์ „์„ ์œ„ํ•œ ์ธ๋•ํ‹ฐ๋ธŒ ๋งํฌ, ๊ทธ๋ฆฌ๊ณ  ์‹ ๊ฒฝ์ž๊ทน์„ ์œ„ํ•œ ์ด์ƒ์„ฑ ์ž๊ทนํŒŒํ˜•์„ ์ƒ์„ฑํ•˜๋Š” ASIC์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ „๋ ฅ ์ ˆ๊ฐ์„ ์œ„ํ•ด ๋‘ ๊ฐœ์˜ ๋ชจ๋“œ๋ฅผ ํ†ตํ•ด ์‚ฌ์šฉ๋ฅ ์„ ์กฐ์ ˆํ•˜๋Š” ๋ฐฉ์‹์ด ์žฅ์น˜์— ์ ์šฉ๋œ๋‹ค. ๋ชจ๋“  ๋ชจ๋“ˆ๋“ค์€ ์ด์‹ ํ›„์˜ ์ƒ๋ฌผํ•™์ , ํ™”ํ•™์  ์•ˆ์ •์„ฑ์„ ์œ„ํ•ด ์•ก์ • ํด๋ฆฌ๋จธ๋กœ ํŒจํ‚ค์ง•๋˜์—ˆ๋‹ค. ์ œ์ž‘๋œ ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ๋ฌด์„  ๋™์ž‘ ํ…Œ์ŠคํŠธ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ์ง€๊ทธ๋น„ ํ†ต์‹ ์˜ ์‹ ํ˜ธ ๋Œ€ ์žก์Œ๋น„๊ฐ€ ์ธก์ •๋˜์—ˆ์œผ๋ฉฐ, ํ•ด๋‹น ํ†ต์‹ ์˜ ๋™์ž‘๊ฑฐ๋ฆฌ ๋ฐ ๋ฐ์ดํ„ฐ ์ŠคํŠธ๋ฆฌ๋ฐ ์„ฑ๋Šฅ์ด ๊ฒ€์‚ฌ๋˜์—ˆ๊ณ , ์žฅ์น˜์˜ ์ถฉ์ „์ด ์ˆ˜ํ–‰๋  ๋•Œ ์ฝ”์ผ๊ฐ„์˜ ๊ฑฐ๋ฆฌ์— ๋”ฐ๋ผ ์ „์†ก๋˜๋Š” ์ „๋ ฅ์˜ ํฌ๊ธฐ๊ฐ€ ์ธก์ •๋˜์—ˆ๋‹ค. ์žฅ์น˜์˜ ํ‰๊ฐ€ ์ดํ›„, ์‹ ๊ฒฝ์ž๊ทน๊ธฐ๋Š” ์ฅ์— ์ด์‹๋˜์—ˆ์œผ๋ฉฐ, ํ•ด๋‹น ๋™๋ฌผ์€ ์ด์‹๋œ ์žฅ์น˜๋ฅผ ์ด์šฉํ•ด ๋ฐฉํ–ฅ ์‹ ํ˜ธ์— ๋”ฐ๋ผ ์ขŒ์šฐ๋กœ ์ด๋™ํ•˜๋„๋ก ํ›ˆ๋ จ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, 3์ฐจ์› ๋ฏธ๋กœ ๊ตฌ์กฐ์—์„œ ์ฅ์˜ ์ด๋™๋ฐฉํ–ฅ์„ ์œ ๋„ํ•˜๋Š” ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ์žฅ์น˜์˜ ๊ธฐ๋Šฅ์„ฑ์„ ์ถ”๊ฐ€์ ์œผ๋กœ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ œ์ž‘๋œ ์žฅ์น˜์˜ ํŠน์ง•์ด ์—ฌ๋Ÿฌ ์ธก๋ฉด์—์„œ ์‹ฌ์ธต์ ์œผ๋กœ ๋…ผ์˜๋˜์—ˆ๋‹ค.Chapter 1 : Introduction 1 1.1. Neural Interface 2 1.1.1. Concept 2 1.1.2. Major Approaches 3 1.2. Neural Stimulator for Animal Brain Stimulation 5 1.2.1. Concept 5 1.2.2. Neural Stimulator for Freely Moving Small Animal 7 1.3. Suggested Approaches 8 1.3.1. Wireless Communication 8 1.3.2. Power Management 9 1.3.2.1. Wireless Power Transmission 10 1.3.2.2. Energy Harvesting 11 1.3.3. Full implantation 14 1.3.3.1. Polymer Packaging 14 1.3.3.2. Modular Configuration 16 1.4. Objectives of This Dissertation 16 Chapter 2 : Methods 18 2.1. Overview 19 2.1.1. Circuit Description 20 2.1.1.1. Pulse Generator ASIC 21 2.1.1.2. ZigBee Transceiver 23 2.1.1.3. Inductive Link 24 2.1.1.4. Energy Harvester 25 2.1.1.5. Surrounding Circuitries 26 2.1.2. Software Description 27 2.2. Antenna Design 29 2.2.1. RF Antenna 30 2.2.1.1. Design of Monopole Antenna 31 2.2.1.2. FEM Simulation 31 2.2.2. Inductive Link 36 2.2.2.1. Design of Coil Antenna 36 2.2.2.2. FEM Simulation 38 2.3. Device Fabrication 41 2.3.1. Circuit Assembly 41 2.3.2. Packaging 42 2.3.3. Electrode, Feedthrough, Cable, and Connector 43 2.4. Evaluations 45 2.4.1. Wireless Operation Test 46 2.4.1.1. Signal-to-Noise Ratio (SNR) Measurement 46 2.4.1.2. Communication Range Test 47 2.4.1.3. Device Operation Monitoring Test 48 2.4.2. Wireless Power Transmission 49 2.4.3. Electrochemical Measurements In Vitro 50 2.4.4. Animal Testing In Vivo 52 Chapter 3 : Results 57 3.1. Fabricated System 58 3.2. Wireless Operation Test 59 3.2.1. Signal-to-Noise Ratio Measurement 59 3.2.2. Communication Range Test 61 3.2.3. Device Operation Monitoring Test 62 3.3. Wireless Power Transmission 64 3.4. Electrochemical Measurements In Vitro 65 3.5. Animal Testing In Vivo 67 Chapter 4 : Discussion 73 4.1. Comparison with Conventional Devices 74 4.2. Safety of Device Operation 76 4.2.1. Safe Electrical Stimulation 76 4.2.2. Safe Wireless Power Transmission 80 4.3. Potential Applications 84 4.4. Opportunities for Further Improvements 86 4.4.1. Weight and Size 86 4.4.2. Long-Term Reliability 93 Chapter 5 : Conclusion 96 Reference 98 Appendix - Liquid Crystal Polymer (LCP) -Based Spinal Cord Stimulator 107 ๊ตญ๋ฌธ ์ดˆ๋ก 138 ๊ฐ์‚ฌ์˜ ๊ธ€ 140Docto

    Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

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    Bidirectional neural interfaces are tools designed to โ€œcommunicateโ€ with the brain via recording and modulation of neuronal activity. The bidirectional interface systems have been adopted for many applications. Neuroscientists employ them to map neuronal circuits through precise stimulation and recording. Medical doctors deploy them as adaptable medical devices which control therapeutic stimulation parameters based on monitoring real-time neural activity. Brain-machine-interface (BMI) researchers use neural interfaces to bypass the nervous system and directly control neuroprosthetics or brain-computer-interface (BCI) spellers. In bidirectional interfaces, the implantable transducers as well as the corresponding electronic circuits and systems face several challenges. A high channel count, low power consumption, and reduced system size are desirable for potential chronic deployment and wider applicability. Moreover, a neural interface designed for robust closed-loop operation requires the mitigation of stimulation artifacts which corrupt the recorded signals. This dissertation introduces several techniques targeting low power consumption, small size, and reduction of stimulation artifacts. These techniques are implemented for extracellular electrophysiological recording and two stimulation modalities: direct current stimulation for closed-loop control of seizure detection/quench and optical stimulation for optogenetic studies. While the two modalities differ in their mechanisms, hardware implementation, and applications, they share many crucial system-level challenges. The first method aims at solving the critical issue of stimulation artifacts saturating the preamplifier in the recording front-end. To prevent saturation, a novel mixed-signal stimulation artifact cancellation circuit is devised to subtract the artifact before amplification and maintain the standard input range of a power-hungry preamplifier. Additional novel techniques have been also implemented to lower the noise and power consumption. A common average referencing (CAR) front-end circuit eliminates the cross-channel common mode noise by averaging and subtracting it in analog domain. A range-adapting SAR ADC saves additional power by eliminating unnecessary conversion cycles when the input signal is small. Measurements of an integrated circuit (IC) prototype demonstrate the attenuation of stimulation artifacts by up to 42 dB and cross-channel noise suppression by up to 39.8 dB. The power consumption per channel is maintained at 330 nW, while the area per channel is only 0.17 mm2. The second system implements a compact headstage for closed-loop optogenetic stimulation and electrophysiological recording. This design targets a miniaturized form factor, high channel count, and high-precision stimulation control suitable for rodent in-vivo optogenetic studies. Monolithically integrated optoelectrodes (which include 12 ยตLEDs for optical stimulation and 12 electrical recording sites) are combined with an off-the-shelf recording IC and a custom-designed high-precision LED driver. 32 recording and 12 stimulation channels can be individually accessed and controlled on a small headstage with dimensions of 2.16 x 2.38 x 0.35 cm and mass of 1.9 g. A third system prototype improves the optogenetic headstage prototype by furthering system integration and improving power efficiency facilitating wireless operation. The custom application-specific integrated circuit (ASIC) combines recording and stimulation channels with a power management unit, allowing the system to be powered by an ultra-light Li-ion battery. Additionally, the ยตLED drivers include a high-resolution arbitrary waveform generation mode for shaping of ยตLED current pulses to preemptively reduce artifacts. A prototype IC occupies 7.66 mm2, consumes 3.04 mW under typical operating conditions, and the optical pulse shaping scheme can attenuate stimulation artifacts by up to 3x with a Gaussian-rise pulse rise time under 1 ms.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147674/1/mendrela_1.pd
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