Primate eye tracking with carbon-nanotube-paper-composite based capacitive sensors and machine learning algorithms

Background: Accurate real-time eye tracking is crucial in oculomotor system research. While the scleral search coil system is the gold standard, its implantation procedure and bulkiness pose challenges. Camera-based systems are affected by ambient lighting and require high computational and electric power. New Method: This study presents a novel eye tracker using proximity capacitive sensors made of carbonnanotube-paper-composite (CPC). These sensors detect femtofarad-level capacitance changes caused by primate corneal movement during horizontal and vertical eye rotations. Data processing and machine learning algorithms are evaluated to enhance the accuracy of gaze angle prediction. Results: The system performance is benchmarked against the scleral coil during smooth pursuits, saccades tracking, and fixations. The eye tracker demonstrates up to 0.97 correlation with the coil in eye tracking and is capable of estimating gaze angle with a median absolute error as low as 0.30 degrees. Comparison: The capacitive eye tracker demonstrates good consistency and accuracy in comparison to the goldstandard scleral search coil method. Conclusions: This lightweight, non-invasive capacitive eye tracker offers potential as an alternative to traditional coil and camera-based systems in oculomotor research and vision science.