Deep learning-based perceptual stimulus encoder for bionic vision

Lucas Relic, Bowen Zhang, Yi-Lin Tuan, Michael Beyeler ACM Augmented Humans (AHs) ‘22

Best Poster Award


Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible visual percepts (phosphenes). Here we propose a perceptual stimulus encoder (PSE) based on convolutional neural networks (CNNs) that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept. We demonstrate the effectiveness of the encoder on MNIST using a psychophysically validated phosphene model tailored to individual retinal implant users. The present work constitutes an essential first step towards improving the quality of the artificial vision provided by retinal implants

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