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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2502.06672 |
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| _version_ | 1866912352281034752 |
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| author | Sadeghi, Roksana Beyeler, Michael |
| author_facet | Sadeghi, Roksana Beyeler, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Spatial Estimation of Perceptual Thresholds for Retinal Implants via Gaussian Process Regression Sadeghi, Roksana Beyeler, Michael Quantitative Methods 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. |
| title | Efficient Spatial Estimation of Perceptual Thresholds for Retinal Implants via Gaussian Process Regression |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2502.06672 |