Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction

TL;DR

This study assesses the consistency of visual fixation predictions across different subjects and devices, revealing that average fixations transfer well across devices but individual fixations do not, informing neuroprosthetic applications.

Contribution

It provides an empirical evaluation of cross-subject and cross-device fixation consistency using a novel embedded eye tracker and standard datasets.

Findings

Average fixations transfer reliably across devices for simple stimuli

Individual fixation patterns show weak cross-device consistency

Results support using average fixation data to improve neuroprosthetic systems

Abstract

Understanding cross-subject and cross-device consistency in visual fixation prediction is essential for advancing eye-tracking applications, including visual attention modeling and neuroprosthetics. This study evaluates fixation consistency using an embedded eye tracker integrated into regular-sized glasses, comparing its performance with high-end standalone eye-tracking systems. Nine participants viewed 300 images from the MIT1003 dataset in subjective experiments, allowing us to analyze cross-device and cross-subject variations in fixation patterns with various evaluation metrics. Our findings indicate that average visual fixations can be reliably transferred across devices for relatively simple stimuli. However, individual-to-average consistency remains weak, highlighting the challenges of predicting individual fixations across devices. These results provide an empirical foundation for leveraging predicted average visual fixation data to enhance neuroprosthetic applications.