Joshua Kay

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2017-27

May 4, 2017

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-27.pdf

Advancements in minimally-invasive, distributed biological interface nodes en- able possibilities for networks of sensors and actuators to connect the brain with external devices. These brain machine interfacing systems require addressing three critical areas for real world use: signal-acquisition hardware, real-time operation, and long term validation. The recent development of the neural dust sensor mote has shown that utilizing ultrasound backscatter communication enables untethered sub-mm neural recording devices. These implanted sensor motes require a wearable external ultrasound interrogation device to enable in-vivo, unconstrained neural interface experiments. However, minimizing the complexity and size of the implanted sensors shifts the power and processing burden to the external interrogator. An ultrasound backscatter interrogator that supports real-time backscatter processing in a rodent-wearable, completely wireless device is presented. The device demonstrates a pulse-amplitude modulated non-return to zero level encoding which is intended for transmitting neural information. The ultrasound link is interfaced by a transducer array, which is driven by a 7-channel, high-voltage ultrasound interface ASIC. The system is controlled by a microcontroller which digitizes and processes the backscatter waveform and sends demodulated data to a remote client via bluetooth. This enables a compact ultrasound interrogation device intended for rodent neural inter- face experiments but applicable to other model systems. The wireless ultrasound interrogation device presented marks an integral step toward the development of practical brain machine interfacing technology.

Advisors: Bernhard Boser

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BibTeX citation:

@mastersthesis{Kay:EECS-2017-27,
    Author= {Kay, Joshua},
    Title= {Rodent Wearable Ultrasound Interrogation System for Wireless Neural Recording},
    School= {EECS Department, University of California, Berkeley},
    Year= {2017},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-27.html},
    Number= {UCB/EECS-2017-27},
    Abstract= {Advancements in minimally-invasive, distributed biological interface nodes en- able possibilities for networks of sensors and actuators to connect the brain with external devices. These brain machine interfacing systems require addressing three critical areas for real world use: signal-acquisition hardware, real-time operation, and long term validation. The recent development of the neural dust sensor mote has shown that utilizing ultrasound backscatter communication enables untethered sub-mm neural recording devices. These implanted sensor motes require a wearable external ultrasound interrogation device to enable in-vivo, unconstrained neural interface experiments. However, minimizing the complexity and size of the implanted sensors shifts the power and processing burden to the external interrogator. An ultrasound backscatter interrogator that supports real-time backscatter processing in a rodent-wearable, completely wireless device is presented. The device demonstrates a pulse-amplitude modulated non-return to zero level encoding which is intended for transmitting neural information. The ultrasound link is interfaced by a transducer array, which is driven by a 7-channel, high-voltage ultrasound interface ASIC. The system is controlled by a microcontroller which digitizes and processes the backscatter waveform and sends demodulated data to a remote client via bluetooth. This enables a compact ultrasound interrogation device intended for rodent neural inter- face experiments but applicable to other model systems. The wireless ultrasound interrogation device presented marks an integral step toward the development of practical brain machine interfacing technology.},
}

EndNote citation:

%0 Thesis
%A Kay, Joshua 
%T Rodent Wearable Ultrasound Interrogation System for Wireless Neural Recording
%I EECS Department, University of California, Berkeley
%D 2017
%8 May 4
%@ UCB/EECS-2017-27
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-27.html
%F Kay:EECS-2017-27