Automated calibration of somatosensory stimulation using reinforcement learning.

ABSTRACT:

Background

The identification of the electrical stimulation parameters for neuromodulation is a subject-specific and time-consuming procedure that presently mostly relies on the expertise of the user (e.g., clinician, experimenter, bioengineer). Since the parameters of stimulation change over time (due to displacement of electrodes, skin status, etc.), patients undergo recurrent, long calibration sessions, along with visits to the clinics, which are inefficient and expensive. To address this issue, we developed an automatized calibration system based on reinforcement learning (RL) allowing for accurate and efficient identification of the peripheral nerve stimulation parameters for somatosensory neuroprostheses.

Methods

We developed an RL algorithm to automatically select neu

SUBMITTER: Borda L 

PROVIDER: S-EPMC10523674 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature