Using reinforcement learning as an alternate method to test and automatically optimize pre-processing parameters for prosthetic vision
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2021-06-01
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en
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Abstract
Researchers are close to developing cortical electrode implants through
which blind people can get some sort of sight back. This sight comes in
the form of phosphene vision which are basically bright light dots that can
be triggered when parts of the visual cortex on the brain are arti cially
stimulated with electrical currents. The resolution of this phosphene vi-
sion is still rather limited. There have been proposed and tested many
pre-processing techniques to best utilize this limited resolution but it can
still be improved. Many of these techniques are tested in experiments with
normal sighted human participants who get to see simulated phosphene
vision on a screen or with a virtual reality headset. Such experiments
are time-consuming and can be expensive. As alternative, we tried using
reinforcement learning to conduct a similar experiment as one of those
experiments where human participants had to do a way nding task with
simulated phosphene vision with di erent phosphene resolutions. The
results we got were promising showing many similarities to the experi-
ment with human participants. However, they were not exactly the same.
Furthermore, we also did an experiment where we let the reinforcement
learning automatically optimize a parameter, the threshold for the Canny
edge detection, of the pre-processing method we used. We found that
in the condition that we changed the light intensity of the environment
there was a signi cant di erence in the threshold that was learned by the
model. For the other conditions no signi cant di erence was found. We
then validated the performance of the learned threshold and found that
for the lowest light intensity this vastly improved the reward the agent
scored, but not for the brightest light intensity. When visually inspecting
the output that came out of the optimal learned threshold for the low-
est light intensity we also found that it may not be entirely optimal for
humans. These results show that using reinforcement learning can result
in useful things in improving phosphene vision, however it still has to be
researched further to optimally make use of it.
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Faculteit der Sociale Wetenschappen