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通过高斯过程回归对视网膜植入物感知值的高效空间估计

Roksana Sadeghi¹, Michael Beyeler^1,2

¹RS and MB are with the Department of Computer Science, University of California, Santa Barbara, CA 93106, USA.

|February 24, 2025

概括

高斯过程回归 (GPR) 通过预测感知值来改善视网膜假体校准. 这种高效的方法减少了患者的时间,并增强了高电极设备的个性化视力恢复.

科学领域:

生物医学工程生物医学工程
神经科学是一个神经科学.
眼科医生眼科眼科

背景情况:

视网膜假肢的目的是通过电刺激恢复视力.
对感知值的校准对于高电极设备至关重要,但耗时.

研究的目的:

开发和评估高斯过程回归 (GPR) 框架,以有效校准视网膜假体.
为了比较GPR与不同的内核和采样策略,以获得值预测准确度.

主要方法:

利用了四名Argus II视网膜假肢使用者的感知值数据.
实现了GPR与母基和辐射基函数 (RBF) 内核.
与空间优化抽样和适应性抽样进行比较,采用统一的随机抽样.

主要成果:

使用Matern内核的GPR在值预测方面显著超过了RBF内核 (p < .001).
与两个参与者的随机抽样相比,空间优化抽样减少了预测误差 (p < .05).
适应性采样显示,与空间采样相比,准确度增长不显著,但对于一名参与者来说接近显著 (p = .074).

结论:

GPR与空间采样相结合,为视网膜假体校准提供了可扩展和高效的方法.
这种方法最大限度地减少了患者的负担,并保持了预测准确度.
该框架为具有空间结构值的神经假肢器件的适应性校准提供了可通用的解决方案.

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