Rikky Muller

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-19

May 1, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-19.pdf

Clinically viable and minimally invasive neural interfaces stand to revolutionize disease care for patients with neurological conditions. For example, recent research in Brain-Machine Interfaces has shown success in using electronic signals from the motor cortex of the brain to control artificial limbs, providing hope for patients with spinal cord injuries. Currently, neural interfaces are large, wired and require open-skull operation. Future, less invasive interfaces with increased numbers of electrodes, signal processing and wireless capability will enable prosthetics, disease control and completely new user-computer interfaces.

The first part of this thesis presents a signal-acquisition front end for neural recording that uses a digitally intensive architecture to reduce system area and enable operation from a 0.5V supply. The entire front-end occupies only 0.013mm2 while including “per-pixel” digitization, and enables simultaneous recording of LFP and action potentials for the first time. The second part presents the development of a minimally invasive yet scalable wireless platform for electrocorticography (ECoG), an electrophysiological technique where electrical potentials are recorded from the surface of the cerebral cortex, greatly reducing cortical scarring and improving implant longevity. A high-density flexible MEMS electrode array is tightly integrated with active circuits and a power-receiving antenna to realize a fully implantable system in a very small footprint. Building on the previously developed digitally intensive architecture, an order of magnitude in circuit area reduction is realized with 3x improvement in power efficiency over state-of-the-art enabling a scalable platform for 64-channel recording and beyond.

Advisors: Jan M. Rabaey

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BibTeX citation:

@phdthesis{Muller:EECS-2015-19,
    Author= {Muller, Rikky},
    Title= {Low Power, Scalable Platforms for Implantable Neural Recording},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-19.html},
    Number= {UCB/EECS-2015-19},
    Abstract= {Clinically viable and minimally invasive neural interfaces stand to revolutionize disease care for patients with neurological conditions. For example, recent research in Brain-Machine Interfaces has shown success in using electronic signals from the motor cortex of the brain to control artificial limbs, providing hope for patients with spinal cord injuries. Currently, neural interfaces are large, wired and require open-skull operation. Future, less invasive interfaces with increased numbers of electrodes, signal processing and wireless capability will enable prosthetics, disease control and completely new user-computer interfaces.

The first part of this thesis presents a signal-acquisition front end for neural recording that uses a digitally intensive architecture to reduce system area and enable operation from a 0.5V supply. The entire front-end occupies only 0.013mm2 while including “per-pixel” digitization, and enables simultaneous recording of LFP and action potentials for the first time. The second part presents the development of a minimally invasive yet scalable wireless platform for electrocorticography (ECoG), an electrophysiological technique where electrical potentials are recorded from the surface of the cerebral cortex, greatly reducing cortical scarring and improving implant longevity. A high-density flexible MEMS electrode array is tightly integrated with active circuits and a power-receiving antenna to realize a fully implantable system in a very small footprint. Building on the previously developed digitally intensive architecture, an order of magnitude in circuit area reduction is realized with 3x improvement in power efficiency over state-of-the-art enabling a scalable platform for 64-channel recording and beyond.},
}

EndNote citation:

%0 Thesis
%A Muller, Rikky 
%T Low Power, Scalable Platforms for Implantable Neural Recording
%I EECS Department, University of California, Berkeley
%D 2015
%8 May 1
%@ UCB/EECS-2015-19
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-19.html
%F Muller:EECS-2015-19