Motion-corrected eye tracking (MoCET) improves gaze accuracy during visual fMRI experiments

Abstract

Human eye movements are essential for understanding cognition, yet achieving high-precision eye tracking in fMRI remains challenging. Even slight head shifts from the initial calibration position can introduce drift in eye tracking data, leading to substantial gaze inaccuracies. To address this, we introduce Motion-Corrected Eye Tracking (MoCET), a novel approach that corrects drift using head motion parameters derived from the preprocessing of fMRI data. MoCET requires no additional hardware and can be applied retrospectively to existing datasets. We show that it outperforms traditional detrending methods with respect to accuracy of gaze estimation and offers higher spatial and temporal precision compared to MR-based eye tracking approaches. By overcoming a key limitation in integrating eye tracking with fMRI, MoCET facilitates investigations of naturalistic vision and cognition in fMRI research.