Hydrogel Actuated Carbon Fiber Microelectrode Array

Abstract: 

Glial passivation and subsequent electrical insulation of implantable microelectrodes is a major bottleneck for long-term viability of neural probes. Self-deploying microelectrodes have been developed to minimize glial scarring and adverse biological effects near neural recording sites, but typically suffer from low electrode densities and deployment distance.

In this dissertation, we propose and evaluate a large displacement, self-deploying architecture using a water absorbing hydrogel to extrude a high density carbon fiber array out of a microfabricated shuttle. To enable mm-scale displacements, this device records electrical signals via mechanically unanchored carbon fiber microelectrodes and couples them through a physiological electrolyte to the backend metal electrodes. A hydrogel-compatible silicon microfabrication and assembly process for a high density microelectrode array is presented, and mechanical insertion into a tissue phantom is demonstrated. An equivalent circuit model and multichannel recording capability are validated using electrochemical impedance and crosstalk measurements.

This hydrogel actuation mechanism can provide sufficient force to push at least 66 fibers concurrently in one direction while achieving tissue phantom penetration depths up to 2.5 mm. The physiological electrolyte bridge used for recording marginally increases total signal path impedance up to 10%, but does not significantly affect recording capabilities nor multichannel crosstalk. This novel mechanical insertion and electrical recording architecture enables deep tissue penetration while simultaneously recording from a dense array of unanchored microelectrodes. This work proposes a mechanically and electrically robust neural probe architecture that could significantly improve microelectrode density and actuation distance of current state-of-the-art self-deploying microelectrode arrays while minimizing potential glial passivation effects.

Author: 
Kristofer S. J. Pister
Publication date: 
May 1, 2023
Publication type: 
Ph.D. Dissertation
Citation: 
Oliver Chen EECS Department University of California, Berkeley Technical Report No. UCB/EECS-2023-59 May 1, 2023

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