Bionic Vision Lab at UC Santa Barbara

We are an interdisciplinary group interested in using brain-computer interfaces as a tool to study the neural mechanisms of visual perception in people with vision loss and to develop advanced stimulation strategies for different bionic eye technologies, with the ultimate goal of restoring useful vision to people who are blind.

Logo © 2019 @drdina, Dina’s SciArt.

PCMag: Building the bionic eye… with car tech?

(Feb 23, 2021) Instead of focusing on one day restoring ‘natural’ vision, we may be better off thinking about how to create ‘practical’ and ‘useful’ artificial vision now. A Smart Prosthesis could provide cues to the visually impaired, much like a computer vision system talks to a self-driving car.

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New publication: Towards immersive virtual reality simulations

(Feb 23, 2021) In this poster to be presented at the Augmented Humans (AHs) International Conference 2021, we outline our strategy on using immersive virtual reality so that sighted subjects can act as ‘virtual patients’ in real-world tasks.

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New publication: Deep learning–based scene simplification for bionic vision

(Feb 18, 2021) We are excited to announce the lab’s first paper, to be presented at the Augmented Humans (AHs) International Conference 2021, where we explore different scene simplification strategies for bionic vision.

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Research

Fig. 1: Degenerative retinal diseases cause irreversible vision loss in more than 10 million people worldwide. Analogous to cochlear implants, retinal prostheses electrically stimulate surviving retinal cells in order to evoke neuronal responses that are inter-preted by the brain as visual percepts (‘phosphenes’).

Towards a Smart Bionic Eye: Artificial Vision for People Who Are Blind

Today, over 10 million people worldwide are living with profound visual impairment, and retinal neuroprostheses (“bionic eye”, Fig. 1) are being developed to restore vision to these individuals. Analogous to cochlear implants, these devices electrically stimulate surviving retinal cells to evoke visual percepts (“phosphenes”). Existing devices generally provide an improved ability to localize high-contrast objects and perform basic orientation & mobility tasks.

However, the quality of current prosthetic vision is still rudimentary. A major outstanding challenge is translating electrode stimulation into a code that the brain can understand. Interactions between the device electronics and the retinal neurophysiology lead to distortions that can severely limit the quality of the generated visual experience.

Rather than aiming to one day restore natural vision (which may remain elusive until we fully understand the neural code of vision), we might be better off thinking about how to create practical and useful artificial vision now. We can already make things appear brighter the closer they get or use computer vision to highlight important objects in the scene. In the future, these visual augmentations could be combined with GPS to give directions, warn users of impending dangers in their immediate surroundings, or even extend the range of visible light with the use of an infrared sensor (think bionic night-time vision). Once the quality of the generated artificial vision reaches a certain threshold, there are a lot of exciting avenues to pursue.

Clinical studies have demonstrated that the vision provided by current SR devices differs substantially from normal sight.

Rather than predicting perceptual distortions, one needs to solve the inverse problem: What is the best stimulus to generate a desired visual percept?

Novel stimulation strategies can be tested on sighted subjects viewing a simulation of prosthetic vision in virtual/augmented reality.
  • Bionic Vision Lab

    • Brain Sciences

      Computational Neuroscience · ML/AI
      Visual Neuroscience

    • Vision Sciences

      Computer Vision · Psychophysics
      Low Vision · Sight Restoration

    • Brain-Computer Interfaces

      Neural Engineering · VR/AR/XR
      Assistive Technologies

Our group combines expertise across disciplines including computer science, computational neuroscience, and psychology. Joining us requires a specific mindset—realizing that we can’t all possibly know everything, but that everyone provides a specific piece to the puzzle (see below for our current openings).

Together we want to do science that matters.

Lab members

Principal Investigator

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Michael Beyeler

Assistant Professor

PhD Students

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CS

Jacob Granley

PhD Student

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DYNS

Justin Kasowski

PhD Student

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CS

Aiwen Xu

PhD Student

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PBS

Byron Johnson

PhD Student

Undergraduate Students

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PBS

Anvitha Akkaraju

Research Assistant

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PBS

Tanya Bhatia

Research Assistant

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ENV

Annika Brydon

Research Assistant

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PBS

Yuchen Hou

Research Assistant

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PBS

Ananth Mahes

Research Assistant

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PBS

Rachel Mochizuki

Honors Student

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CS

Iris Moini-Nazeri

Research Assistant

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PBS

Ryan Neydavood

Research Assistant

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PBS, MATH

Bill Nguyen

Research Assistant

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PBS

Angel Solares

Research Assistant

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CS

Yuval Steinhart

Research Assistant

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MCDB

Archita Tharanipathy

Research Assistant

Alumni

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Rami Dabit

Research Assistant
CE @ UCSB
(Spring 2021)

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Ethan Gao

Visiting Scholar
Ojai Valley School
(Summer 2020)

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Lu Han

Lab Volunteer
CE @ UCSB
(Spring 2021)

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Zuying Hu

MS Student
CS @ UCSB
(2020)

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Dylan Lin

Research Assistant
CS @ UCSB
(2020 - 2021)

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Ori Mizrahi

Research Assistant
CS @ UCSB
(2020)

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Sairisheek Muttukuru

Research Assistant
CE @ UCSB
(Spring 2021)

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Kha Nguyen

Visiting Scholar
UC San Diego
(Summer 2020)

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Ruben Olmos

Research Assistant
PSTAT @ UCSB
(2021)

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Ziming Qi

MS Student
CE @ UCSB
(2020 - 2021)

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Fatima Qubadi

Research Assistant
PBS @ UCSB
(Spring 2021)

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Rashi Raghulan

Research Assistant
MCDB @ UCSB
(2019 - 2020)

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Versha Rohatgi

Visiting Scholar
Mountain View High
(Summer 2020)

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Shuyun Tang

Research Assistant
PSTAT @ UCSB
(2021)

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Nathan Wu

Honors Student
CS @ UCSB
(2020 - 2021)

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Kelly Yan

Research Assistant
CS @ UCSB
(Spring 2021)

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Yusong Yan

Lab Volunteer
CS @ UCSB
(Summer 2020)

Selected Publications

All publications

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
Preprint PDF

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
Preprint PDF Code

We propose to embed biologically realistic models of simulated prosthetic vision in immersive virtual reality so that sighted subjects can act as ‘virtual patients’ in real-world tasks.
Justin Kasowski, Nathan Wu, Michael Beyeler ACM AHs ‘21
Preprint Video

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
Preprint Code Dataset Video

Teaching

All courses

This graduate course will introduce students to the multidisciplinary field of bionic vision, with an emphasis on both the computer science and neuroscience of the field.

Overview of computer vision problems and techniques for analyzing the content of images and video. Topics include image formation, edge detection, image segmentation, pattern recognition, texture analysis, optical flow, stereo vision, shape …

Join Us

We are looking for curious and talented individuals who share our passion for bionic vision. If you are interested in joining us, check out our Lab Manual to familiarize yourself with our lab policies.

PhD Students

We are hiring! The next opportunity to join our lab is Fall 2022 (application deadline: December 2021).

We are looking to fill an NIH-funded PhD position in computational neuroscience. The project involves developing computational and data-driven models of the mouse visual cortex in collaboration with Spencer Smith (UCSB), Michael Goard (UCSB), and Cris Niell (U Oregon). Contact Michael for more info.

For all other positions, please first apply to one of the following Graduate programs:

You will then be able to indicate your wish to work with Michael in the “Major and Degree Objective” tab under “Faculty Interests”.

Please know that we get a lot of emails from prospective PhD students. If you decide to contact Michael before applying to the program, you can make your application stand out by demonstrating that you have spent some time on our website and thought hard about why bionic vision is a good fit for your skills and interest.

CS Master's Students

We have several MS positions available for Fall 2021:

  • Human-computer interaction: Build VR/AR/XR applications applied to low vision and bionic vision (Unity, compute shaders, image processing, bionic vision simulations, eye tracking)
  • Biomedical image analysis: Build predictive models applied to biomedical image datasets (registration, segmentation, self-supervised learning)-
  • Neuromorphic engineering: Build brain-inspired event-based vision systems applied to scene understanding (silicon retina, spiking neural networks)

Please contact Michael to set up a time to meet.

Undergraduate Students

We have a limited number of RA positions available for Fall 2021 (e.g., via PSY-199 or CS-196):

  • Visual psychophysics: Design and run behavioral studies using VR/AR simulations of bionic vision (Unity, vision science)
  • Develop applications for pulse2percept (Python)
  • Web development: Maintain and extend bionic-vision.org (React, HTML, CSS, MongoDB)

In general, students stay for several quarters (one quarter is not enough to get up to speed) and work for academic credit or on a volunteer basis. Students should have a GPA of ≥ 3.0 (University requirement).

If you are interested, please contact Michael with your prior experience and your transcript (unofficial is fine) and we can arrange a meeting.

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