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    ์†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

    The neural engine: a reprogrammable low power platform for closed-loop optogenetics

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    Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2ยฐ C for medical implants and maintain long battery life. In this work, we developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. By integrating with custom designed brain implant chip, we have demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system long-term recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units

    An Energy Efficient non-volatile FPGA Digital Processor for Brain Neuromodulation

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    PhD ThesisBrain stimulation technologies have the potential to provide considerable clinical benefits for people with a range of neurological disorders. Recent neuroscience studies have shown that considerable information of brain states is contained in the low frequency local field potential (If-LFP; below 5Hz) recordings with application in real-time closed-loop neurostimulation for treating neurological disorders. Given these signals can be sampled at low sampling rate and hence provide sparse data streams, there is an opportunity to design implantable neuroprosthesis with long battery lifecycles which enables enough processing power to implement long-term, real-time closed loop control algorithms. In this thesis, a closed-loop embedded digital processor has been created for use in rodent neuroscience experiments. The first contribution of this work is to develop a mathematical analytical design approach of feedback controller for suppressing high-amplitude epileptic activity in the neuron mass model to form a better understanding of how to perform a better closed-loop stimulation to control seizures. The second contribution and the third contribution are combined to present an exploratory energy-efficient digital processor architecture built with commercial off-the-shelf non-volatile FPGAs and microcontroller for sparse data processing of brain neuromodulation. A digital hardware design of an exemplar PID control algorithm has been implemented on this proposed digital architecture. A new power computing diagram of this time-driven approach significantly reduced the power consumption which suggests that a digital combined control system of non-volatile FPGAs and microcontroller outweighs a digital control system of microcontroller with microcontroller regarding computing time cost and energy consumption supposing one microcontroller is always required. Taken together, this digital energy-efficient processor architecture gives important insights and viewpoints for the further advancements of neuroprosthesis for brain neurostimulation to achieve lower power consumption for sparse sampling data rate

    Detection, Prediction and Control of Epileptic Seizures

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    abstract: From time immemorial, epilepsy has persisted to be one of the greatest impediments to human life for those stricken by it. As the fourth most common neurological disorder, epilepsy causes paroxysmal electrical discharges in the brain that manifest as seizures. Seizures have the effect of debilitating patients on a physical and psychological level. Although not lethal by themselves, they can bring about total disruption in consciousness which can, in hazardous conditions, lead to fatality. Roughly 1\% of the world population suffer from epilepsy and another 30 to 50 new cases per 100,000 increase the number of affected annually. Controlling seizures in epileptic patients has therefore become a great medical and, in recent years, engineering challenge. In this study, the conditions of human seizures are recreated in an animal model of temporal lobe epilepsy. The rodents used in this study are chemically induced to become chronically epileptic. Their Electroencephalogram (EEG) data is then recorded and analyzed to detect and predict seizures; with the ultimate goal being the control and complete suppression of seizures. Two methods, the maximum Lyapunov exponent and the Generalized Partial Directed Coherence (GPDC), are applied on EEG data to extract meaningful information. Their effectiveness have been reported in the literature for the purpose of prediction of seizures and seizure focus localization. This study integrates these measures, through some modifications, to robustly detect seizures and separately find precursors to them and in consequence provide stimulation to the epileptic brain of rats in order to suppress seizures. Additionally open-loop stimulation with biphasic currents of various pairs of sites in differing lengths of time have helped us create control efficacy maps. While GPDC tells us about the possible location of the focus, control efficacy maps tells us how effective stimulating a certain pair of sites will be. The results from computations performed on the data are presented and the feasibility of the control problem is discussed. The results show a new reliable means of seizure detection even in the presence of artifacts in the data. The seizure precursors provide a means of prediction, in the order of tens of minutes, prior to seizures. Closed loop stimulation experiments based on these precursors and control efficacy maps on the epileptic animals show a maximum reduction of seizure frequency by 24.26\% in one animal and reduction of length of seizures by 51.77\% in another. Thus, through this study it was shown that the implementation of the methods can ameliorate seizures in an epileptic patient. It is expected that the new knowledge and experimental techniques will provide a guide for future research in an effort to ultimately eliminate seizures in epileptic patients.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

    A Closed-Loop Bidirectional Brain-Machine Interface System For Freely Behaving Animals

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    A brain-machine interface (BMI) creates an artificial pathway between the brain and the external world. The research and applications of BMI have received enormous attention among the scientific community as well as the public in the past decade. However, most research of BMI relies on experiments with tethered or sedated animals, using rack-mount equipment, which significantly restricts the experimental methods and paradigms. Moreover, most research to date has focused on neural signal recording or decoding in an open-loop method. Although the use of a closed-loop, wireless BMI is critical to the success of an extensive range of neuroscience research, it is an approach yet to be widely used, with the electronics design being one of the major bottlenecks. The key goal of this research is to address the design challenges of a closed-loop, bidirectional BMI by providing innovative solutions from the neuron-electronics interface up to the system level. Circuit design innovations have been proposed in the neural recording front-end, the neural feature extraction module, and the neural stimulator. Practical design issues of the bidirectional neural interface, the closed-loop controller and the overall system integration have been carefully studied and discussed.To the best of our knowledge, this work presents the first reported portable system to provide all required hardware for a closed-loop sensorimotor neural interface, the first wireless sensory encoding experiment conducted in freely swimming animals, and the first bidirectional study of the hippocampal field potentials in freely behaving animals from sedation to sleep. This thesis gives a comprehensive survey of bidirectional BMI designs, reviews the key design trade-offs in neural recorders and stimulators, and summarizes neural features and mechanisms for a successful closed-loop operation. The circuit and system design details are presented with bench testing and animal experimental results. The methods, circuit techniques, system topology, and experimental paradigms proposed in this work can be used in a wide range of relevant neurophysiology research and neuroprosthetic development, especially in experiments using freely behaving animals

    The Design and Implementation of an Extensible Brain-Computer Interface

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    An implantable brain computer interface: BCI) includes tissue interface hardware, signal conditioning circuitry, analog-to-digital conversion: ADC) circuitry and some sort of computing hardware to discriminate desired waveforms from noise. Within an experimental paradigm the tissue interface and ADC hardware will rarely change. Recent literature suggests it is often the specific implementation of waveform discrimination that can limit the usefulness and lifespan of a particular BCI design. If the discrimination techniques are implemented in on-board software, experimenters gain a level of flexibility not currently available in published designs. To this end, I have developed a firmware library to acquire data sampled from an ADC, discriminate the signal for desired waveforms employing a user-defined function, and perform arbitrary tasks. I then used this design to develop an embedded BCI built upon the popular Texas Instruments MSP430 microcontroller platform. This system can operate on multiple channels simultaneously and is not fundamentally limited in the number of channels that can be processed. The resulting system represents a viable platform that can ease the design, development and use of BCI devices for a variety of applications

    Closed-loop approaches for innovative neuroprostheses

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    The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

    Transcranial Auricular Vagus Nerve Stimulation (taVNS) and Ear-EEG: Potential for Closed-Loop Portable Non-invasive Brain Stimulation

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    No matter how hard we concentrate, our attention fluctuates โ€“ a fact that greatly affects our success in completing a current task. Here, we review work from two methods that, in a closed-loop manner, have the potential to ameliorate these fluctuations. Ear-EEG can measure electric brain activity from areas in or around the ear, using small and thus portable hardware. It has been shown to capture the state of attention with high temporal resolution. Transcutaneous auricular vagus nerve stimulation (taVNS) comes with the same advantages (small and light) and critically current research suggests that it is possible to influence ongoing brain activity that has been linked to attention. Following the review of current work on ear-EEG and taVNS we suggest that a combination of the two methods in a closed-loop system could serve as a potential application to modulate attention

    Modern Telemetry

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    Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

    Developing neurostimulation techniques to investigate antidepressant and mood modulating behaviors / by Rajas Prakash Kale

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     My PhD consisted of a multidisciplinary approach towards primary research in the field of translational neuroscience. Incorporation of preclinical research, behavioral neuroscience, translational psychiatry, neural engineering, and biomedical device development techniques drives my continuing passion towards helping patients through innovation
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