Dissertation Defense

Bidirectional Neural Interface Circuits with On-Chip Stimulation Artifact Reduction Schemes

Adam Mendrela


Bidirectional neural interfaces are tools designed to "communicate" with the brain via recording and modulation of neuronal activity. Such systems have been employed for basic neuroscience research, therapeutic medical devices, and other brain-machine interface applications. Further development and dissemination are hindered by several system-level challenges. First, compact, power efficient, and high-channel-count solutions are needed to provide high performance functionality in wireless and chronic deployment. Second, bidirectional interfaces inherently suffer from large unwanted artifact noise during simultaneous recording and stimulation.

This dissertation attempts to solve the problem of how to reduce noise and artifacts in bidirectional interfaces without greatly sacrificing efficiency and small form factor. Three ASIC-based prototypes are developed to address the different facets of this challenge. First, a front-end stimulation artifact cancellation scheme is introduced to efficiently remove artifacts in electrical stimulation and recording systems. The second prototype focuses on the miniaturization of a headstage system for high-precision optogenetic stimulation and electrophysiological recording. Finally, an optical pulse shaping scheme is developed for an opto-electrophysiology interface IC to reduce stimulation artifacts in integrated  µLED optoelectrodes.

Sponsored by

Professors Euisik Yoon and Michael Flynn