Roksana Sadeghi | Searcharxiv

arXiv2025

Simulated prosthetic vision confirms checkerboard as an effective raster pattern for epiretinal implants

Spatial scheduling of electrode activation ("rastering") is essential for safely operating high-density retinal implants, yet its perceptual consequences remain poorly understood. This study systematically evaluates the impact of raster patterns, or spatial arrangements of sequential electrode activation, on performance and perceived difficulty in simulated prosthetic vision (SPV). By addressing this gap, we aimed to identify patterns that optimize functional vision in retinal implants. Sighted participants completed letter recognition and motion discrimination tasks under four raster patterns (horizontal, vertical, checkerboard, and random) using an immersive SPV system. The simulations emulated epiretinal implant perception and employed psychophysically validated models of electrode activation, phosphene appearance, nonlinear spatial summation, and temporal dynamics, ensuring realistic representation of prosthetic vision. Performance accuracy and self-reported difficulty were analyzed to assess the effects of raster patterning. The checkerboard pattern consistently outperformed other raster patterns, yielding significantly higher accuracy and lower difficulty ratings across both tasks. The horizontal and vertical patterns introduced biases aligned with apparent motion artifacts, while the checkerboard minimized such effects. Random patterns resulted in the lowest performance, underscoring the importance of structured activation. Notably, checkerboard matched performance in the "No Raster" condition, despite conforming to groupwise safety constraints. This is the first quantitative, task-based evaluation of raster patterns in SPV. Checkerboard-style scheduling enhances perceptual clarity without increasing computational load, offering a low-overhead, clinically relevant strategy for improving usability in next-generation retinal prostheses.

cs.HC↗

arXiv2025

Efficient Spatial Estimation of Perceptual Thresholds for Retinal Implants via Gaussian Process Regression

Retinal prostheses restore vision by electrically stimulating surviving neurons, but calibrating perceptual thresholds (i.e., the minimum stimulus intensity required for perception) remains a time-intensive challenge, especially for high-electrode-count devices. Since neighboring electrodes exhibit spatial correlations, we propose a Gaussian Process Regression (GPR) framework to predict thresholds at unsampled locations while leveraging uncertainty estimates to guide adaptive sampling. Using perceptual threshold data from four Argus II users, we show that GPR with a Matern kernel provides more accurate threshold predictions than a Radial Basis Function (RBF) kernel (p < .001, Wilcoxon signed-rank test). In addition, spatially optimized sampling yielded lower prediction error than uniform random sampling for Participants 1 and 3 (p < .05). While adaptive sampling dynamically selects electrodes based on model uncertainty, its accuracy gains over spatial sampling were not statistically significant (p > .05), though it approached significance for Participant 1 (p = .074). These findings establish GPR with spatial sampling as a scalable, efficient approach to retinal prosthesis calibration, minimizing patient burden while maintaining predictive accuracy. More broadly, this framework offers a generalizable solution for adaptive calibration in neuroprosthetic devices with spatially structured stimulation thresholds, paving the way for faster, more personalized system fitting in future high-channel-count implants. Clinical relevance: Gaussian Progress Regression offers a scalable path toward faster, more personalized calibration procedures for future high-channel-count neuroprosthetic devices.

q-bio.QM↗