Will it ever be possible to upload information to my brain?

20 September 2021

Prof. Beyeler was featured in a Giz Asks article about the prospect of using brain-machine interfaces to directly write in information to the brain.

Illustration: © Benjamin Currie/Gizmodo

PCMag: Building the bionic eye… with car tech?

23 February 2021

Interview with Prof. Beyeler: Instead of focusing on one day restoring natural vision, we may be better off thinking about how to create useful artificial vision now.

Deep learning–based scene simplification for bionic vision

18 February 2021

We are excited to announce the lab’s first paper, selected for a Best Paper Honorable Mention Award at the Augmented Humans (AHs) International Conference 2021, where we explore different scene simplification strategies for bionic vision.

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Who We Are

We are an interdisciplinary group interested in using brain-computer devices as a tool to study the neural mechanisms of visual perception in people with vision loss, with the ultimate goal of restoring useful vision to people living with incurable blindness using bionic technologies.

Our group combines expertise in computer science, psychology, human-computer interaction, computational neuroscience, and neuroengineering. 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. Together we want to do science that matters.

Brain Sciences

Computational Neuroscience
Brain-Inspired Computing · ML/AI

Vision Sciences

Human & Machine Vision
Low Vision · Sight Restoration

Brain-Computer Devices

Visual Neuroprostheses
VR/AR/MR · Assistive Technologies

Recent Publications

We present a phenomenological model that predicts phosphene appearance as a function of stimulus amplitude, frequency, and pulse duration.

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.

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.

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