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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Retinal prostheses aim to restore vision via electrical stimulation.
    • Calibration of perceptual thresholds is crucial but time-consuming for high-electrode-count devices.

    Purpose of the Study:

    • To develop a scalable and efficient framework for retinal prosthesis calibration using Gaussian Process Regression (GPR).
    • To predict perceptual thresholds at unsampled electrode locations and guide adaptive sampling.

    Main Methods:

    • Proposed a GPR framework with a Matérn kernel to predict perceptual thresholds.
    • Utilized perceptual threshold data from four Argus II retinal prosthesis users.
    • Compared GPR performance against a Radial Basis Function (RBF) kernel and evaluated different sampling strategies (spatial vs. uniform random vs. adaptive).

    Main Results:

    • GPR with a Matérn kernel significantly outperformed the RBF kernel in threshold prediction accuracy (p < .001).
    • Spatially optimized sampling reduced prediction error compared to uniform random sampling for two participants (p < .05).
    • Adaptive sampling showed no significant accuracy gains over spatial sampling but approached significance for one participant (p = .074).

    Conclusions:

    • GPR combined with spatial sampling offers an efficient and accurate method for retinal prosthesis calibration.
    • This approach minimizes patient burden and facilitates personalized system fitting for high-channel-count neuroprosthetic devices.
    • The GPR framework provides a generalizable solution for adaptive calibration in other neuroprosthetic systems with spatially structured stimulation.