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High-precision eye-tracking in nonvisual settings

Project description

AI-aided eye movement monitoring during sleep

Eye movement analysis can be used to provide valuable insight into cognitive and mental states. While traditional eye tracking technologies rely on open eyes and visual input, they fail in contexts such as sleep or meditation. Electrooculography (EOG), which measures electrical activity around the eyes, offers a promising alternative. To eliminate the need for visual tracking yet map precise eye movements, the ERC-funded DEEP-EOG project proposes to combine EOG signals with deep learning. To train neural networks, researchers will utilise EOG and eye tracking data across diverse scenarios. This approach enables non-invasive, accurate monitoring of eye movements with significant implications for sleep research.

Objective

In the rapidly evolving landscape of cognitive neuroscience, a significant surge in research has highlighted eye movements as a critical yet previously underestimated window into neuro-cognitive processes. Our project is at the forefront of this paradigm shift, proposing an innovative machine learning-based approach to decode eye movements with high precision using electrooculography (EOG) channels alone. This method is set to revolutionize eye-tracking technologies, particularly in non-visual contexts such as closed eyes or during sleep, where traditional methods are ineffective.

The initial phase of our research involves the extensive collection of simultaneous eye-tracking and EOG data under various conditions, including simulated sleep patterns. Using state-of-the-art deep neural networks to map high-precision eye-tracking data onto the simultaneously collected EOG signal, we aim to achieve a level of EOG electrode precision that has previously been unattainable. A key focus of our deep-learning model is its ability to generalize, thereby minimizing the need for extensive individual calibration.

In practical terms, our project holds transformative potential across multiple domains. In the scientific field, it enables the exploration of eye movements with closed eyes, which is necessary for mind-reading research and mental states like meditation, providing new insights into the human mind. In medical monitoring, it opens avenues for non-invasive sleep analysis and the diagnosis of sleep-related disorders, previously hindered by the low precision of current gaze-tracking technology. The most groundbreaking application lies in developing a wearable device that enables widespread, effortless tracking and analysis of eye movements during sleep, thus democratizing the use of advanced cognitive monitoring tools to offer insights into sleep patterns and build a healthy sleep routine.

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Topic(s)

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

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(opens in new window) ERC-2024-POC

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Host institution

UNIVERSITA DEGLI STUDI DI TRENTO
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 150 000,00
Address
VIA CALEPINA 14
38122 TRENTO
Italy

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Region
Nord-Est Provincia Autonoma di Trento Trento
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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Beneficiaries (1)

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