NeuraViPeR | Neural Active Visual Prosthetics for Restoring Function

Summary
Approaches that aim to restore vision for blind individuals with electrical stimulation of the brain have hit a technology wall. Existing systems only stimulate a small set of neurons in the brain, and interfaces have a longevity of only a few months. NeuraViPeR aims to lay ground-breaking foundation for a radically new paradigm which consists not only of constructing a neuroprosthesis with thousands of electrodes but also the creation of adaptive machine learning algorithms for a new brain-computer interfacing technology, which will remain safe and effective for decades. Several technological breakthroughs will be established. First, innovative approaches for stimulation with high-electrode-count interfacing with the visual cortex; creating thin (~10 µm thick, < 50 µm wide) flexible probes that cause minimal tissue damage; new electrode coatings that will be stable in spite of long-term repeated electrical stimulation; and novel microchip methods for combining online channeling of the stimulation currents to many thousands of electrodes, combined with monitoring of neuronal activity in higher cortical areas. Second, new deep learning algorithms that transform the camera footage into stimulation patterns for the cortex and that use feedback on recorded brain states and eye tracking to improve perception in a closed-loop approach. The algorithms will extract maximally relevant information to enable blind individuals to recognize objects and facial expressions and navigate through unfamiliar environments. The software algorithms will be translated onto low-latency, power-efficient neuromorphic deep learning hardware, to create a neuroprosthesis system that is lightweight, robust, and portable. NeuraViPeR will tackle these challenges through interdisciplinary teams with complementary expertise in computational, systems and clinical neuroscience, materials engineering, microsystems design, and deep learning.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/899287
Start date: 01-09-2020
End date: 28-02-2025
Total budget - Public funding: 3 998 681,25 Euro - 3 998 681,00 Euro
Cordis data

Original description

Approaches that aim to restore vision for blind individuals with electrical stimulation of the brain have hit a technology wall. Existing systems only stimulate a small set of neurons in the brain, and interfaces have a longevity of only a few months. NeuraViPeR aims to lay ground-breaking foundation for a radically new paradigm which consists not only of constructing a neuroprosthesis with thousands of electrodes but also the creation of adaptive machine learning algorithms for a new brain-computer interfacing technology, which will remain safe and effective for decades. Several technological breakthroughs will be established. First, innovative approaches for stimulation with high-electrode-count interfacing with the visual cortex; creating thin (~10 µm thick, < 50 µm wide) flexible probes that cause minimal tissue damage; new electrode coatings that will be stable in spite of long-term repeated electrical stimulation; and novel microchip methods for combining online channeling of the stimulation currents to many thousands of electrodes, combined with monitoring of neuronal activity in higher cortical areas. Second, new deep learning algorithms that transform the camera footage into stimulation patterns for the cortex and that use feedback on recorded brain states and eye tracking to improve perception in a closed-loop approach. The algorithms will extract maximally relevant information to enable blind individuals to recognize objects and facial expressions and navigate through unfamiliar environments. The software algorithms will be translated onto low-latency, power-efficient neuromorphic deep learning hardware, to create a neuroprosthesis system that is lightweight, robust, and portable. NeuraViPeR will tackle these challenges through interdisciplinary teams with complementary expertise in computational, systems and clinical neuroscience, materials engineering, microsystems design, and deep learning.

Status

SIGNED

Call topic

FETOPEN-01-2018-2019-2020

Update Date

27-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.2. EXCELLENT SCIENCE - Future and Emerging Technologies (FET)
H2020-EU.1.2.1. FET Open
H2020-FETOPEN-2018-2020
FETOPEN-01-2018-2019-2020 FET-Open Challenging Current Thinking