Dongjin (DJ) Seo

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2018-146

December 1, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-146.pdf

A seamless, high density, chronic interface to the nervous system is essential to enable clinically relevant applications such as electroceuticals or brain-machine interfaces (BMI). Currently, a major hurdle in neurotechnology is the lack of an implantable neural interface system that remains viable for a patient's lifetime due to the development of biological response near the implant. Recently, mm-scale implantable electromagnetics (EM) based wireless neural interfaces have been demonstrated in an effort to extend system longevity, but the implant size scaling (and therefore density) is ultimately limited by the power available to the implant.

In this thesis, we propose neural dust, an entirely new method of wireless power and data telemetry using ultrasound, which can address fundamental issues associated with using EM to interrogate miniaturized implants. Key concepts and fundamental system design trade-offs and ultimate size, power, and bandwidth scaling limits of such system are analyzed from first principles. We demonstrate both theoretically and experimentally that neural dust scales extremely well, down to 100's, if not 10's of $\mu$m. We highlight first wireless recordings from nerve and muscle in an animal model using neural dust prototype. The thesis concludes with strategies for multi-neural dust interrogation and future directions of neural dust beyond neuromodulation.

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BibTeX citation:

@phdthesis{Seo:EECS-2018-146,
    Author= {Seo, Dongjin (DJ)},
    Title= {Neural Dust: Ultrasonic Biological Interface},
    School= {EECS Department, University of California, Berkeley},
    Year= {2018},
    Month= {Dec},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-146.html},
    Number= {UCB/EECS-2018-146},
    Abstract= {A seamless, high density, chronic interface to the nervous system is essential to enable clinically relevant applications such as electroceuticals or brain-machine interfaces (BMI). Currently, a major hurdle in neurotechnology is the lack of an implantable neural interface system that remains viable for a patient's lifetime due to the development of biological response near the implant. Recently, mm-scale implantable electromagnetics (EM) based wireless neural interfaces have been demonstrated in an effort to extend system longevity, but the implant size scaling (and therefore density) is ultimately limited by the power available to the implant. 

In this thesis, we propose neural dust, an entirely new method of wireless power and data telemetry using ultrasound, which can address fundamental issues associated with using EM to interrogate miniaturized implants. Key concepts and fundamental system design trade-offs and ultimate size, power, and bandwidth scaling limits of such system are analyzed from first principles. We demonstrate both theoretically and experimentally that neural dust scales extremely well, down to 100's, if not 10's of $\mu$m. We highlight first wireless recordings from nerve and muscle in an animal model using neural dust prototype. The thesis concludes with strategies for multi-neural dust interrogation and future directions of neural dust beyond neuromodulation.},
}

EndNote citation:

%0 Thesis
%A Seo, Dongjin (DJ) 
%T Neural Dust: Ultrasonic Biological Interface
%I EECS Department, University of California, Berkeley
%D 2018
%8 December 1
%@ UCB/EECS-2018-146
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-146.html
%F Seo:EECS-2018-146