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High-precision eye-tracking in nonvisual settings

CORDIS · observation · Publication date unknown

search 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 acro

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR