Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces (eBook)

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2010 | 2011
VII, 121 Seiten
Springer New York (Verlag)
978-1-4419-6727-5 (ISBN)

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Ultra Low-Power Integrated Circuit Design for Wireless Neural Interfaces -  Jeremy Holleman,  Brian Otis,  Fan Zhang
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This book will describe ultra low-power, integrated circuits and systems designed for the emerging field of neural signal recording and processing, and wireless communication. Since neural interfaces are typically implanted, their operation is highly energy-constrained. This book introduces concepts and theory that allow circuit operation approaching the fundamental limits. Design examples and measurements of real systems are provided. The book will describe circuit designs for all of the critical components of a neural recording system, including: Amplifiers which utilize new techniques to improve the trade-off between good noise performance and low power consumption. Analog and mixed-signal circuits which implement signal processing tasks specific to the neural recording application: Detection of neural spikes Extraction of features that describe the spikes Clustering, a machine learning technique for sorting spikes Weak-inversion operation of analog-domain transistors, allowing processing circuits that reduce the requirements for analog-digital conversion and allow low system-level power consumption. Highly-integrated, sub-mW wireless transmitter designed for the Medical Implant Communications Service (MICS) and ISM bands.
This book will describe ultra low-power, integrated circuits and systems designed for the emerging field of neural signal recording and processing, and wireless communication. Since neural interfaces are typically implanted, their operation is highly energy-constrained. This book introduces concepts and theory that allow circuit operation approaching the fundamental limits. Design examples and measurements of real systems are provided. The book will describe circuit designs for all of the critical components of a neural recording system, including:Amplifiers which utilize new techniques to improve the trade-off between good noise performance and low power consumption. Analog and mixed-signal circuits which implement signal processing tasks specific to the neural recording application:Detection of neural spikes Extraction of features that describe the spikesClustering, a machine learning technique for sorting spikes Weak-inversion operation of analog-domain transistors, allowing processing circuits that reduce the requirements for analog-digital conversion and allow low system-level power consumption. Highly-integrated, sub-mW wireless transmitter designed for the Medical Implant Communications Service (MICS) and ISM bands.

Contents 5
Introduction 8
Bio-Signal Interface Amplifiers: An Introduction 15
Characteristics of the Recording Electrodes 15
Characteristics of Bio-Signals 17
Brain Recordings 17
Muscle-Based Signals 18
Noise/Power Tradeoff 18
Flicker Noise, 1/f Noise 18
Thermal Noise 19
Representative Prior Art 19
A Low-Power, Low-Noise, Open-Loop Amplifier for Neural Recording 21
Open-Loop Amplifier Design 21
Results 23
Effect of Non-Linearity on Neural Recordings 26
Conclusions 29
Closed-Loop Neural Recording Amplifier Design Techniques 31
Design of a Closed-Loop Telescopic Amplifier 31
Closed-Loop Architecture 31
Analysis of Pseudo-Resistors 32
Telescopic OTA Design Overview 33
Design Optimization 34
Stability and Common-Mode Feedback 35
Design of a Closed-Loop Complementary-Input Amplifier 36
Design of an Closed-Loop Fully-Differential Complementary-Input Amplifier 36
Design of a Variable-Gain Amplifier 39
Closed-Loop Bio-Signal Amplifiers: Experimental Results 42
Amplifier Testing 42
Variable Gain Amplifier (VGA) Testing 44
In-Vivo Testing 46
Design and Implementation of Chopper-Stabilized Amplifiers 50
Chopper-Stabilization Technique 50
Open-Loop Operation Principle 50
Closed-Loop Operation Principle 51
Design of a Chopper-Stabilized Amplifier 51
Hardware Implementation 53
Transfer Function 53
Amplifier Noise 54
Spike Detection and Characterization 55
The Spike Detection Task 55
Spike Detection Techniques 57
Analog and Mixed-Mode Computation 58
System Design 59
Spike Detector 60
Feature Extraction 61
Analog-Digital Converter 62
Results 63
Spike Sorting 68
Overview 68
K-Means Clustering Algorithm 70
Hardware Considerations for Analog On-Line Clustering 72
On-Line Median Learning 72
Non-Ideal Computational Elements 74
Asymmetric Updates 75
Analog Clustering Circuit 78
Floating-Gate Memories 78
Device Characterization 79
Circuit Design 82
Clustering Circuit 82
Floating-Gate Memory Cell 84
Decision Circuit 86
Experimental Results 88
Update Rates 88
Memory Cell Retention 90
Classification 91
Clustering Convergence 93
Discussion 96
NeuralWISP: A Wirelessly Powered Spike Density Recording System 100
Previous Neural Recording Systems 100
System Design 102
Analog Signal Path 103
Digital Control 106
Test Results 106
Experimental Results 110
Conclusions 111
A 500mW Wireles Neural Streaming System 114
Analog Front End 114
Conversion and Control 115
MICS-band Wireless Transmitter 116
Results 116
Conclusions 119
Index 121

Erscheint lt. Verlag 29.10.2010
Zusatzinfo VII, 121 p.
Verlagsort New York
Sprache englisch
Themenwelt Informatik Weitere Themen CAD-Programme
Technik Elektrotechnik / Energietechnik
Schlagworte Circuit Design • Circuits and Systems • Embedded Systems • Low-Noise Amplifiers • low-power analog circuits • Low-Power Circuit Design • Low-Power Radio Frequency • low-power signal processing • Neural Recording Systems • Ultra Low-Power Circuit Design
ISBN-10 1-4419-6727-3 / 1441967273
ISBN-13 978-1-4419-6727-5 / 9781441967275
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