Human-in-the-loop optimization of visual prosthetic stimulation
Human-in-the-loop optimization of visual prosthetic stimulation
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DOI：
10.1088/1741-2552/ac7615
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发表时间：
2022-06-01
影响因子：
4
通讯作者：
Chalk, Matthew
中科院分区：
文献类型：
--
作者：
Fauvel, Tristan;Chalk, Matthew
目标。视网膜假体是视网膜退行性疾病患者恢复视力的一种很有前途的策略。这些设备通过电刺激视网膜中的神经元来弥补光感受器的损失。目前，使用这种设备可以恢复的视觉功能非常有限。这在一定程度上是由于电流扩散，意外的轴突激活，以及现有设备的有限分辨率。在这里，我们使用最近的假体视觉模型表明，优化设备对视觉刺激的编码方式可以帮助克服其中的一些限制，导致视觉感知的显著改善。接近。我们提出了一种在实践中做到这一点的策略，在视觉任务中使用患者的反馈。我们方法的主要挑战来自这样一个事实，即通常情况下，人们只能接触到患者有限数量的嘈杂反应。我们提出了两种方法来解决这个问题：首先，我们使用了一种假眼模型来约束和简化优化。我们证明，如果一个人知道这个模型的参数，就有可能极大地改善他们的知觉表现。其次，我们提出了优先贝叶斯优化，以有效地学习每个患者的这些模型参数，使用最少的试验。主要结果。为了测试我们的方法，我们给健康的受试者提供了由最近的假体视觉模型产生的视觉刺激，以复制安装了植入物的患者的感知体验。我们的优化过程导致了感知图像质量的显著和强劲的改善，这转化为在其他任务中性能的提高。意义重大。重要的是，我们的策略与假体的类型无关，因此很容易在现有的植入物中实施。
Objective. Retinal prostheses are a promising strategy to restore sight to patients with retinal degenerative diseases. These devices compensate for the loss of photoreceptors by electrically stimulating neurons in the retina. Currently, the visual function that can be recovered with such devices is very limited. This is due, in part, to current spread, unintended axonal activation, and the limited resolution of existing devices. Here we show, using a recent model of prosthetic vision, that optimizing how visual stimuli are encoded by the device can help overcome some of these limitations, leading to dramatic improvements in visual perception. Approach. We propose a strategy to do this in practice, using patients' feedback in a visual task. The main challenge of our approach comes from the fact that, typically, one only has access to a limited number of noisy responses from patients. We propose two ways to deal with this: first, we use a model of prosthetic vision to constrain and simplify the optimization. We show that, if one knew the parameters of this model for a given patient, it would be possible to greatly improve their perceptual performance. Second we propose a preferential Bayesian optimization to efficiently learn these model parameters for each patient, using minimal trials. Main results. To test our approach, we presented healthy subjects with visual stimuli generated by a recent model of prosthetic vision, to replicate the perceptual experience of patients fitted with an implant. Our optimization procedure led to significant and robust improvements in perceived image quality, that transferred to increased performance in other tasks. Significance. Importantly, our strategy is agnostic to the type of prosthesis and thus could readily be implemented in existing implants.