Spatial fitting by percept location tracking
The present invention is an improved spatial fitting and training system for a visual prosthesis. The system of the present invention maps projected locations of percepts, where a person perceives a percept from a visual prosthesis to the intended location of the percepts. The projected location may vary over time. This test results can be used to correct a visual prosthesis or spatially map the visual prosthesis.
1. A method fitting a visual prosthesis comprising:
providing a touch screen monitor connected to a computer:
projecting an image on the touch screen monitor in an actual location;
asking a subject to indicate the perceived location of the image by touching the touch screen monitor;
providing feed back to subject as the accuracy of the perceived location;
calculating a difference between the actual location and the perceived location in the computer; and
adjusting the visual prosthesis based on the difference.
2. The method according to claim 1 , further comprising:
repeating the steps of claim 1 , at different times;
storing the difference in the computer for each of the different times; and
refining the adjustment of the visual prosthesis by stored difference values.
3. The method according to claim 1 , further comprising:
recording a camera sampling position of the visual prosthesis camera; and
refining the adjustment of the visual prosthesis by the camera sampling position.
4. The method according to claim 1 , wherein the feedback is auditory feedback.
5. The method according to claim 4 , wherein the auditory feedback is an indication of right or wrong.
6. The method according to claim 4 , wherein the auditory feedback is an indication of the direction and distance of the error.
7. The method according to claim 1 , further comprising:
calculating a confidence interval based on error rates; and
excluding data with a confidence interval less that a predetermined minimum.
8. The method according to claim 7 , wherein the predetermined minimum is 95%.
9. The method according to claim 7 , wherein the confidence interval is calculated by t-distributions.
10. The method according to claim 7 , further comprising calculating separate confidence intervals for vertical and horizontal errors and excluding data with vertical confidence interval of less than a predetermined amount, and excluding data with a horizontal confidence interval of less than a predetermined amount.
11. The method according to claim 10 , wherein the predetermined amount is 95%.