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Power Conditioning and Stimulation f...

Biederman, William James, III.

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Power Conditioning and Stimulation for Wireless Neural Interface ICs.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Power Conditioning and Stimulation for Wireless Neural Interface ICs./
Author:	Biederman, William James, III.
Description:	92 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.
Contained By:	Dissertation Abstracts International76-08B(E).
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3686191
ISBN:	9781321629392

Power Conditioning and Stimulation for Wireless Neural Interface ICs.
Biederman, William James, III.

Power Conditioning and Stimulation for Wireless Neural Interface ICs. - 92 p.

Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2014.

This item must not be sold to any third party vendors.

Brain machine interfaces have the potential to revolutionize our understanding of the brain, restore motor function, and improve the quality of life to patients with neurological con- ditions. In recent human trials, control of robotic prostheses has been demonstrated using micro-electrode arrays, which provide high spatio-temporal resolution and an electrical feed- back path to the brain. However, after implantation, scar tissue degrades the recording signal-to-noise ratio and limits the useful lifetime of the array. This work presents two systems which utilize wireless techniques to mitigate this effect and create high-density, long-term interfaces with the human brain.

ISBN: 9781321629392Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Power Conditioning and Stimulation for Wireless Neural Interface ICs.
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020 $a 9781321629392
035 $a (MiAaPQ)AAI3686191
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040 $a MiAaPQ $c MiAaPQ
100 1 $a Biederman, William James, III. $3 3171261
245 1 0 $a Power Conditioning and Stimulation for Wireless Neural Interface ICs.
300 $a 92 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-08(E), Section: B.
500 $a Adviser: Jan M. Rabaey.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2014.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a Brain machine interfaces have the potential to revolutionize our understanding of the brain, restore motor function, and improve the quality of life to patients with neurological con- ditions. In recent human trials, control of robotic prostheses has been demonstrated using micro-electrode arrays, which provide high spatio-temporal resolution and an electrical feed- back path to the brain. However, after implantation, scar tissue degrades the recording signal-to-noise ratio and limits the useful lifetime of the array. This work presents two systems which utilize wireless techniques to mitigate this effect and create high-density, long-term interfaces with the human brain.
520 $a A wirelessly powered 0.125mm2 65nm CMOS IC integrates four 1.5uW amplifiers (6.5uVrms input-referred noise with 10kHz bandwidth) with power conditioning and communication cir- cuitry. Multiple nodes free-float in the brain and communicate via backscatter to a wireless interrogator using a frequency-domain multiple access communication scheme. The full sys- tem, verified with wirelessly powered in vivo recordings, consumes 10.5uW and operates at 1mm range in air with 50mW transmit power.
520 $a A 65nm CMOS 4.78mm2 neuromodulation SoC integrates closed loop BMI functionality on a single IC which can be arrayed on a wireless sub-cranial platform. The IC consumes 348uA from an unregulated 1.2V supply while operating 64 acquisition channels with epoch compression (at an average firing rate of 50Hz) and engaging two stimulators (with a pulse width of 250us/phase, differential current of 150uA, and a pulse frequency of 100Hz). Com- pared to the state of the art neural SoCs, this represents the lowest area and power for the highest integration complexity achieved to date.
590 $a School code: 0028.
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Engineering, Biomedical. $3 1017684
690 $a 0544
690 $a 0771
690 $a 0541
710 2 $a University of California, Berkeley. $b Electrical Engineering and Computer Sciences. $3 2096274
773 0 $t Dissertation Abstracts International $g 76-08B(E).
790 $a 0028
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3686191