# Eye Tracking Post Processing to Detect Visual Artifacts and Quantify Visual Attention under Cognitive Task Activity during fMRI

## TL;DR

This study creates a new eye-tracking system to better understand visual attention during brain scans, improving accuracy and supporting future cognitive research.

## Contribution

A novel eye-tracking post-processing platform was developed to enhance data accuracy and integrate with fMRI for cognitive task analysis.

## Key findings

- Post-processing significantly improved eye-tracking data accuracy.
- Participants maintained high visual attention on the screen during tasks.
- Prolonged tasks led to decreased attention, indicating cognitive load effects.

## Abstract

Determining visual attention during cognitive tasks using activation MRI remains challenging. This study aimed to develop a new eye-tracking (ET) post-processing platform to enhance data accuracy, validate the feasibility of subsequent ET-fMRI applications, and provide tool support. Sixteen volunteers aged 18 to 20 were exposed to a visual temporal paradigm with changing images of objects and faces in various locations while their eye movements were recorded using an MRI-compatible ET system. The results indicate that the accuracy of the data significantly improved after post-processing. Participants generally maintained their visual attention on the screen, with mean gaze positions ranging from 89.1% to 99.9%. In cognitive tasks, the gaze positions showed adherence to instructions, with means ranging from 46.2% to 50%. Temporal consistency assessments indicated prolonged visual tasks can lead to decreased attention during certain tasks. The proposed methodology effectively identified and quantified visual artifacts and losses, providing a precise measure of visual attention. This study offers a robust framework for future work integrating filtered eye-tracking data with fMRI analyses, supporting cognitive neuroscience research.