FARA: A Fast Artifact Recovery Algorithm with Optimum Stimulation Waveform for Single-Cell Resolution Massively Parallel Neural Interfaces

This paper introduces a fast artifact recovery algorithm (FARA) that uses electrochemical impedance spectroscopy to model the electrode-tissue interface and design an optimum stimulation waveform to minimize the residual artifact duration in single-cell resolution neural interfaces. Results in saline solution with a custom PCB and a 30 $\mu \mathrm{m}$ diameter microelectrode array show a worst case artifact recovery time of 160 $\mu \mathrm{s}$ when measured from the end of the working phase (anodic 500 $\mathrm{n}\mathrm{A}, 250\mu \mathrm{s})$. On average, the proposed algorithm provides an 81% improvement over a triphasic charge-balanced stimulation waveform.