# Motion-corrected eye tracking improves gaze accuracy during visual fMRI experiments

## TL;DR

A new method called MoCET improves eye tracking accuracy in fMRI by correcting errors from head movements.

## Contribution

MoCET introduces a novel motion correction technique for eye tracking in fMRI without requiring additional hardware.

## Key findings

- MoCET outperforms conventional detrending methods in gaze estimation accuracy.
- It provides higher spatial and temporal precision than magnetic resonance-based eye tracking.
- The method can be applied to existing datasets retrospectively.

## Abstract

Human eye movements are essential for understanding cognition, yet achieving high-precision eye tracking in functional Magnetic Resonance Imaging (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 present Motion-Corrected Eye Tracking (MoCET), which corrects drift using head motion parameters derived from fMRI preprocessing. MoCET requires no additional hardware and can be applied retrospectively to existing datasets. We show that it outperforms conventional detrending methods with respect to accuracy of gaze estimation and offers higher spatial and temporal precision compared to magnetic resonance-based eye tracking approaches. By overcoming a key limitation in integrating eye tracking with fMRI, MoCET enables precise investigations of naturalistic vision and cognition in fMRI research.

Head movements during eye tracking in fMRI frequently distort gaze signals. This paper presents motion-corrected eye tracking, a method that corrects head motion-induced errors to produce accurate gaze data in fMRI experiments.