We present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to ‘see through the eyes’ of a bionic eye user.
We are an interdisciplinary group interested in the computational modeling of human, animal, machine, and prosthetic vision to elucidate the science behind bionic technologies that may one day restore useful vision to people living with incurable blindness.
Our group combines expertise in computer science/engineering, neuroscience, and psychology. All our team members are computationally minded and have a keen interest in vision and medical applications. Our research projects range from predicting neurophysiological data with deep learning to building biophysical models of electrical brain stimulation, and from studying perception in people with visual impairment to developing prototypes of novel visual accessibility aids using virtual and augmented reality.
Importantly, our ongoing collaborations with several visual prosthesis manufacturers put our laboratory in a unique position to empirically validate our theoretical findings across multiple bionic eye technologies.
We present VR-SPV, an open-source virtual reality toolbox for simulated prosthetic vision that uses a psychophysically validated computational model to allow sighted participants to ‘see through the eyes’ of a bionic eye user.
Justin Kasowski, Michael Beyeler ACM AHs ‘22
We show that sighted individuals can learn to adapt to the unnatural on- and off-cell population responses produced by electronic and optogenetic sight recovery technologies.
Rebecca B. Esquenazi, Kimberly Meier, Michael Beyeler, Geoffrey M. Boynton, Ione Fine JoV 21(10)
We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.
Jacob Granley, Michael Beyeler IEEE EMBC ‘21
We propose HBA-U-Net: a U-Net backbone with hierarchical bottleneck attention to highlight retinal abnormalities that may be important for fovea and optic disc segmentation in the degenerated retina.
Shuyun Tang, Ziming Qi, Jacob Granley, Michael Beyeler MICCAI OMIA ‘21
We combined deep learning-based scene simplification strategies with a psychophysically validated computational model of the retina to generate realistic predictions of simulated prosthetic vision.
Nicole Han, Sudhanshu Srivastava, Aiwen Xu, Devi Klein, Michael Beyeler ACM AHs ‘21