Recalibration of neuromodulation parameters in neural implants with adaptive Bayesian Optimization.

Our automatic algorithm can successfully adapt neurostimulation parameters to perceptual threshold changes and to evoked-sensation location changes over-time. These findings show an automatic and fast way of tackling the inevitable variability of neurostimulation parameters over time. It will potentially enable a widespread and a better usability of this technology upon validation in other frameworks, while decreasing the time and the costs of the treatment. This work suggests the exploitation of AI-based methods for developing the next generation of "smart" neuromodulation devices.