Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Minkyu Je | - |
| dc.contributor.author | Lee, Tae Ju | - |
| dc.date.accessioned | 2017-05-10T08:52:42Z | - |
| dc.date.issued | 2016 | - |
| dc.identifier.uri | http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002228890 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11750/1457 | - |
| dc.description.abstract | Neural recording is an indispensable function required for the brain machine interface (BMI) and neuroscience research. In order to obtain high-quality neural signals with minimal noise, a low-noise performance needs to be provided by a front-end amplifier used for neural recording. At the same time, a lowpower operation must be achieved to avoid tissue damage. This paper presents the neural recording amplifier design optimized to achieve highest possible signal-to-noise ratio (SNR) based on advanced noise modeling which takes into account the effect of a finite source impedance determined by the characteristics of the tissue-electrode interface. The research on the finite source impedance was performed through animal experiments by using rats. The commercial products of NeuroNexus Technologies were implanted over the sensorimotor cortex. Then, the impedances were daily checked for 21 days by using TDT (Tucker-Davis Technologies) system. The front-end amplifier was designed for neural recording over the frequency range from 1 Hz to 10 kHz. The amplifier was implemented with TSMC 0.18-μm CMOS process. We also introduce the systematic procedure for optimizing the neural recording amplifier under the constraint of given power budget, while considering various electrode site areas and the change of the source impedance value over time. ⓒ 2016 DGIST |
- |
| dc.description.tableofcontents | I. INTRODUCTION 1-- 1.1 Trend on the Brain Machine Interface and Neuroscience Research 1-- 1.2 Developed Neural Systems 8-- 1.3 Research Goal 11-- 1.4 Thesis Organization 13-- -- II. TISSUE-ELECTRODE INTERFACE 14-- 2.1 Materials and Methods 14-- 2.1.1 Animals 14-- 2.1.2 Surgical Procedures 16-- 2.1.3 Impedance Measurements 18-- 2.2 Measurements of the Source Impedance & Neural Signals 19-- 2.3 Modeling of the Source Impedance 26-- 2.4 Source Capacitance of the Commercial Products 31-- 2.5 Summary 37-- -- III. NOISE MODELING FOR NEURAL RECORDING AMPLIFIER 38-- 3.1 Simplified Circuit Model for Noise Modeling 38-- 3.2 Noise Contribution depending on the Source Impedance 40-- 3.2.1 Voltage Component 43-- 3.2.2 Correlation Component 43-- 3.2.3 Current Component 44-- 3.3 Noise Modeling without the Source Impedance 45-- 3.3.1 Optimization with Thermal Noise 46-- 3.3.2 Optimization with Thermal & Flicker Noises 52-- 3.3.3 Optimization with Power Budget & Signal Bandwidth 57-- 3.4 Noise Modeling with the Source Impedance 59-- 3.4.1 Analysis of Signal-to-Noise Ratio 59-- 3.4.2 Optimization given Source Impedance 62-- 3.4.3 Optimization with Chip Area 66-- 3.5 Summary 67-- -- IV. DESIGN OF NEURAL RECORDING AMPLIFIER 69-- 4.1 Topology of Neural Recording Amplifier 69-- 4.2 Biasing Circuit 71-- 4.3 Optimization in Signal-to-Noise Ratio 72-- 4.4 Optimization in the Power Budget 80-- 4.5 Optimization in the Chip Area 87-- -- V. SIMULATION RESULTS 90-- -- VI. CONCLUSION AND FUTURE WORK 94-- 6.1 Conclusion 94-- 6.2 Future Work 95-- -- References 96 |
- |
| dc.format.extent | 98 | - |
| dc.language | eng | - |
| dc.publisher | DGIST | - |
| dc.subject | Brain machine interface | - |
| dc.subject | low-noise | - |
| dc.subject | low-power analog circuit | - |
| dc.subject | tissue-electrode interface | - |
| dc.subject | neural recording amplifier | - |
| dc.subject | noise modeling | - |
| dc.subject | 뇌-기계 인터페이스 | - |
| dc.subject | 저잡음 | - |
| dc.subject | 저전력 아날로그 회로 | - |
| dc.subject | 조직-전극 인터페이스 | - |
| dc.subject | 신경 신호 증폭기 | - |
| dc.subject | 잡음 모델링 | - |
| dc.title | Low-Power Neural Recording Amplifier Design Based on Advanced Noise Modeling | - |
| dc.title.alternative | 잡음 모델링 기반 저전력 신경 신호 증폭기 | - |
| dc.type | Thesis | - |
| dc.identifier.doi | 10.22677/thesis.2228890 | - |
| dc.description.alternativeAbstract | 뇌-기계 인터페이스 및 신경 과학 연구에서 신경 신호 측정은 반드시 수행되어야 하는 부분이다. 미세한 크기의 신경 신호를 획득하기 위해서는 저잡음 성능을 만족하는 신경 신호 증폭기가 설계되어야 한다. 동시에 세포 손상을 줄이기 위해 저전력으로 구동되는 아날로그 프런트-엔드가 필요하다. 이 논문은 잡음 모델링에 기반하여 높은 신호 대 잡음비를 얻기 위해 최적화된 신경 신호 증폭기의 설계를 제시한다. 잡음 모델링은 조직-전극 인터페이스에 의해 발생되는 임피던스를 고려하여 최적의 신호 대 잡음비를 얻을 수 있도록 한다. 조직-전극 인터페이스에 의한 임피던스를 측정하기 위해 8마리의 쥐가 실험에 사용되었다. NeuroNexus사의 전극이 쥐의 감각운동 피질 영역에 삽입되었으며, TDT (Tucker-Davis Technologies)사의 측정 장비를 이용하여 21일 연속으로 임피던스 및 뇌파를 측정하였다. 신경 신호 증폭기는 뇌파의 전기신호 특성을 고려하여 1Hz에서 10kHz의 주파수 대역을 포함하도록 TSMC 0.18-μm CMOS 공정을 이용하여 설계되었다. 또한 주어진 전력, 전극의 넓이에 따른 임피던스 및 시간에 따른 임피던스 변화를 고려하여 신경 신호 증폭기를 최적화하는 체계적인 설계절차를 제시한다. ⓒ 2016 DGIST | - |
| dc.description.degree | Master | - |
| dc.contributor.department | Information and Communication Engineering | - |
| dc.contributor.coadvisor | Choi, Hong Soo | - |
| dc.date.awarded | 2016. 2 | - |
| dc.publisher.location | Daegu | - |
| dc.description.database | dCollection | - |
| dc.date.accepted | 2016-02-12 | - |
| dc.contributor.alternativeDepartment | 대학원 정보통신융합공학전공 | - |
| dc.rights.accessRights | The original item will not be provided upon request from the author | - |
| dc.contributor.affiliatedAuthor | Lee, Tae Ju | - |
| dc.contributor.affiliatedAuthor | Minkyu Je | - |
| dc.contributor.affiliatedAuthor | Choi, Hong Soo | - |
| dc.contributor.alternativeName | 이태주 | - |
| dc.contributor.alternativeName | 제민규 | - |
| dc.contributor.alternativeName | 최홍수 | - |
- Files in This Item:
-
There are no files associated with this item.
- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.