Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback.

Abstract

Objective.The challenge in cortical neuroprosthetic vision is determining the optimal, safe stimulation patterns to evoke the desired light perceptions ('phosphenes') in blind individuals. Clinical studies gain insights into the perceptual characteristics of phosphenes through patient descriptions on provided stimulation protocols. However, the huge parameter space for multi-electrode stimulation makes it difficult to identify the optimality of the stimulation that lead to well-perceived phosphenes. A systematic search in the parameter space of the electrical stimulation is needed to achieve good perception. Bayesian optimization (BO) is a framework for finding optimal parameters efficiently. Using patient's perceptual feedback, a model of patient response based on iteratively generated stimulation protocols can be built to maximize perception quality.Approach.A patient implanted with an intracortical 96-channel microelectrode array in their visual cortex was iteratively presented with stimulation protocols, generated via BO vs. random generation (RG) in two separate experiments. Whereas standard BO methods do not scale well to problems with over a dozen inputs, we optimize a set of 40 electrode currents using trust region-based BO. The protocols determine the electrodes to stimulate and with how much current (0-50 µA), on a total current limit of 500 µA. The patient rated each stimulation's perceptual quality on a Likert scale, where 7 indicated the highest quality and 0 no perception.Main results.The patient ratings gradually converged on higher values with BO, compared to the RG experiment. BO gradually generated protocols with higher total current, in line with the patient preference for higher currents due to brighter phosphenes. Electrodes previously observed as effective in producing phosphene perception were chosen more by BO also with higher current allocation.Significance.This study demonstrates the power of BO in converging to optimal stimulation protocols based on patient feedback, providing an efficient search for stimulation parameters for clinical studies.