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Human-in-the-loop optimization of visual prosthetic stimulation.

Tristan Fauvel1, Matthew Chalk1

  • 1Institut de la Vision Sorbonne Université, INSERM, CNRS 17 rue Moreau, F-75012 Paris, France.

Journal of Neural Engineering
|June 6, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing visual stimuli encoding for retinal prostheses significantly improves vision restoration in patients with degenerative diseases. This new method enhances perceived image quality and task performance, offering a broadly applicable solution.

Keywords:
Bayesian optimisationcomputational neurosciencehuman-in-the-loop optimisationvisual prosthesis

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Ophthalmology

Background:

  • Retinal prostheses aim to restore sight for patients with retinal degenerative diseases by electrically stimulating retinal neurons.
  • Current devices offer limited visual function due to issues like current spread and low resolution.
  • Optimizing visual stimulus encoding is crucial for enhancing prosthetic vision.

Purpose of the Study:

  • To develop and validate a strategy for optimizing visual stimulus encoding in retinal prostheses.
  • To improve visual perception and task performance in patients with retinal degenerative diseases.
  • To create an efficient method for learning patient-specific prosthesis model parameters.

Main Methods:

  • Utilized a model of prosthetic vision to constrain and simplify the optimization process.
  • Employed preferential Bayesian optimization to efficiently learn patient-specific model parameters with minimal trials.
  • Tested the approach on healthy subjects using a prosthetic vision model to simulate patient experience.

Main Results:

  • The proposed optimization strategy led to significant and robust improvements in perceived image quality.
  • Enhanced image quality translated to increased performance in visual tasks.
  • The method demonstrated the potential for dramatic improvements in visual perception.

Conclusions:

  • Optimizing visual stimulus encoding can overcome limitations of current retinal prostheses.
  • The developed preferential Bayesian optimization is efficient for learning patient-specific parameters.
  • This prosthesis-agnostic strategy is readily implementable in existing retinal implant systems.