Visual Prosthesis Shape Analysis for Spatial Fitting
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Solution Overview
Problem
Existing visual prostheses with multiple electrodes face challenges in adjusting each electrode for optimal size, brightness, and shape of percepts without requiring extensive patient interaction, as individual responses to neural stimulation vary significantly across the retina, making manual adjustment impractical for complex electrode arrays.
Innovation Solution
A method involving an array of electrodes in a visual prosthesis that selects and displays geometric shapes to the subject, allowing them to describe perceived shapes, which are then compared to a set of reference shapes using optical character recognition and sequence tracking detection accuracy to adjust the electrode stimulation for improved spatial fitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of each electrode is performed to optimize perception quality, then the precision of spatial fitting is improved, but the time required for fitting increases significantly
Solution Approach 1:
The system performs automatic electrode adjustment using algorithms that analyze patient responses to presented shapes and autonomously optimize stimulation parameters, eliminating the need for time-consuming manual adjustment by clinicians while maintaining high spatial fitting precision
Solution Approach 2:
The system systematically varies stimulation parameters such as amplitude, pulse width, and electrode activation patterns to automatically determine optimal settings for each electrode based on patient perceptual responses, replacing manual parameter tuning with automated parameter optimization
2Measurement precision
If the number of electrodes in the visual prosthesis array is increased to improve resolution, then the image quality is improved, but the complexity of individual electrode characterization increases
Solution Approach 1:
The system divides the complex task of characterizing multiple electrodes into smaller sub-tasks by presenting specific geometric shapes that activate subsets of electrodes, allowing systematic analysis of individual electrode contributions while managing the overall complexity of the multi-electrode array
Solution Approach 2:
The system introduces an automated characterization algorithm as an intermediary between the clinician and the multiple electrodes, which systematically analyzes patient responses to shape presentations and automatically determines optimal parameters for each electrode, reducing the burden of characterizing large numbers of electrodes
3Manufacturing precision
If extensive patient interaction is required for electrode adjustment, then the accuracy of individual electrode optimization is improved, but the ease of operation is reduced
Solution Approach 1:
The system implements automated feedback loops where patient responses to presented shapes are systematically recorded and analyzed to automatically adjust stimulation parameters, maintaining high optimization accuracy while reducing the operational burden compared to manual iterative adjustment
Solution Approach 2:
The system performs self-adjustment of electrode parameters by automatically analyzing patient perceptual responses and autonomously optimizing stimulation settings, eliminating the need for complex manual operations while preserving optimization accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables automatic adjustment of the visual prosthesis to provide accurate and consistent perception of complex shapes without lengthy patient interaction, improving spatial fitting and reducing the complexity of characterizing each electrode individually.
Implementation Method 1
Neural tissue can be artificially stimulated and activated by prosthetic devices that pass pulses of electrical current through electrodes on the prosthetic devices. The passage of current causes changes in electrical potentials across visual neuronal membranes, which can initiate visual neuron action potentials.
Data Source
AI summary
A method of testing subjects' perception of complex shapes created by patterned multi-electrode direct stimulation of a retinal prosthesis is described. The complex shapes can be geometric shapes or characters such as letters of the alphabet and numbers.