Low Power Circuits and Systems for Brain Machine Interfaces

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Material Information

Title:
Low Power Circuits and Systems for Brain Machine Interfaces
Physical Description:
1 online resource (149 p.)
Language:
english
Creator:
Xiao, Zhiming
Publisher:
University of Florida
Place of Publication:
Gainesville, Fla.
Publication Date:

Thesis/Dissertation Information

Degree:
Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Electrical and Computer Engineering
Committee Chair:
BASHIRULLAH,RIZWAN
Committee Co-Chair:
HARRIS,JOHN GREGORY
Committee Members:
BOSMAN,GIJSBERTUS
VAN OOSTROM,JOHANNES H

Subjects

Subjects / Keywords:
analog -- bmi -- neural
Electrical and Computer Engineering -- Dissertations, Academic -- UF
Genre:
Electrical and Computer Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract:
Establishing adirect pathway between the brain and a machine is a promising technique for assisting, augmenting, or repairing human cognitive or sensory-motor functions.The core of this emerging paradigm is a low power and highly integrated brain–machine interface (BMI) that can less invasively sense neural signals above a minimum duration. This work focuses on the design of low-power circuits and systems for optimizing the energy efficiency, recording lifetime, size, and transmission range of BMI systems.  This dissertation first provides an overview of BMI systems and then discusses each BMI building block in terms of power dissipation, noise, size, and reliability requirements. The neural amplifier, typically the first stage of a neural acquisition system, is given particular attention, and, with the goal of achieving both low noise and energy efficiency in amplifier design, this work presents three new amplifier structures: (1) a cascading structure that helps reduce power consumption without sacrificing noise performance by sharing the bias current between two channels; (2) a time multiplexing structure that helps reduce the die size and power consumption by sharing a single operational transconductance amplifier among multiple input channels; and (3) a supply current modulation structure that decreases the amplifier’s average power consumption using atrack and hold function. To analyze switching effects in both the time-multiplexed and supply current–modulated amplifiers, detailed derivations of their respective transfer functions and noise aliasing characteristics are carried out. These derivation procedures are simplified by equating the switched amplifier to a switched RC filter model, and the results show that both schemes cause noise from higher frequency bands to alias down to the base band. The overall noise efficiency factor, however, remains unchanged because of the power saving benefits of the two architectures. This dissertation also presents two new neural recording systems. The first is a battery powered, four-channel device with an analog front end, a digital signalprocessor, and a wireless transceiver. The second system is a single channel neural recording tag that can be powered through a battery or wirelessly from an external resource.
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility:
by Zhiming Xiao.
Thesis:
Thesis (Ph.D.)--University of Florida, 2014.
Local:
Adviser: BASHIRULLAH,RIZWAN.
Local:
Co-adviser: HARRIS,JOHN GREGORY.
Electronic Access:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2014-11-30

Record Information

Source Institution:
UFRGP
Rights Management:
Applicable rights reserved.
Classification:
lcc - LD1780 2014
System ID:
UFE0046153:00001