Brainwave-Augmented Eye Tracker: High-Frequency SSVEPs Improves Camera-Based Eye Tracking Accuracy

Abstract

In this work, we leverage neural mechanisms of visual attention to improve the accuracy of a commercial eye tracker through the analysis of electroencephalography (EEG) waves. Gaze targets were rendered in a computer screen with imperceptible flickering stimuli (≥ 40Hz) that elicited attention-modulated steady-state visual evoked potentials (SSVEPs). Our hybrid system combines EEG and eye-tracking modalities to overcome accuracy limitations of the gaze-tracker alone. We integrate EEG and gaze data to efficiently exploit their complementary strengths driving a Bayesian probabilistic decoder that estimates the target gazed by the user. Our system's performance was analyzed across the screen with varying target sizes, spacings and dataset epoch lengths, using data from 10 subjects. Overall, our hybrid approach improves the classification accuracy of the eye tracker alone for all target parameters and dataset epoch lengths in 11 units on average. The system shows a larger impact at peripheral screen regions where performance enhancement is maximal, reaching improvements of over 45 units. The findings of this work demonstrate that the intrinsic accuracy limitations of camera-based eye-trackers can be corrected with the integration of EEG data, and opens opportunities for gaze tracking applications with higher target granularity.