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Microelectrodes serve as a promising platform to accommodate the growing field of neuroprosthesis control and brain-computer interfaces. The goal of using smaller sized electrodes allows interfaces to interact with smaller neural populations. For example, it is possible for a single microelectrode array to have one channel record electrophysiology from a sensory tract while simultaneously having another channel sample from a nearby motor tract. When an electrode is implanted inside of the body, it is met with an immune cascade, i.e., foreign-body encapsulation, which disrupts the electrode-tissue interface and leads to diminished electrode stimulation and recording capabilities. In most cases these electrode interfaces need to be prematurely replaced or explanted. Thus, as we scale down the electrode’s physical footprint to the micron level, this innate immune encapsulation leads further to diminishing electrical capabilities. The research presented here aims to ameliorate these limitations by adopting an electrochemical-impedance-spectroscopy (EIS) measurement model (MM) to not only validate our electrochemical impedance measurements, but also to generate a biocircuit analog to understand our system. The combination of raw EIS data paired with a rigorous measurement model designed for EIS may elucidate dynamic electrochemical changes occurring before, during and after implantation, thereby highlighting the value of this tool for the greater neural-engineering community.
Chapter 1 serves as an introduction to EIS as it applies to neural electrodes and provides a brief overview of the innate foreign-body response due to implantation and the inclusion of conductive-polymer coatings for interfaces is included. Chapter 2 explores how we may augment acquisition of in vitro EIS data to better emulate EIS data measured during in vivo implantation cases. Chapter 3 highlights a specific conductive polymer (PEDOT:PSS) and its capacity to improve electrochemical performance. Chapter 4 delves into an in vitro protein-adsorption model paired with the MM to quantify the protein monolayer’s impact on EIS measurements. The takeaways from these chapters highlight the MM as vital tool for the greater neural-engineering community by bolstering our knowledge for neural implant creation, modification, and application.