Objective. Neuromodulation technology hold promise to treat several conditions where physiological mechanisms of neural activity have been affected. Personalization of neurostimulation protocols is a mandatory step to make the treatment efficient and the devices highly effective. The interfaces between the targeted nervous tissue and the neurotechnology (i.e., human-machine link or neural interface) usually requires a constant re-calibration of the neuromodulation parameters, due to many different biological and microtechnological phenomena happening over-time. This adaptation of the optimal stimulation parameters generally involves an expert-mediated re-calibration, with correspondent economic burden, compromised every-day usability and efficacy of the device, and consequent loss of time and increased discomfort of patients going back to clinics to get the device tuned. We aim to construct an adaptable AI-based system, able to compensate for these changes autonomously. Approach. We exploited Gaussian Process-based Bayesian Optimization (GPBO) methods to re-adjust the neurostimulation parameters in realistic neuroprosthetic data by integrating the temporal information into the process to tackle the issue of time variability. To this aim, we built a predictive model able to tune the neuromodulation parameters for two separate crucial scenarios where re-calibration is needed. In the first one, we built a model able to find the optimal active sites to use from a multichannel electrode, i.e. able to cover a certain function for a neuroprosthesis, which in this specific case was the evoked-sensation location variability. In the second one, we propose an algorithm able to adapt the injected charge required to obtain a functional neural activation (e.g., perceptual threshold variability). Main results. 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.