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.
@article{arxiv.2502.05554,
title = {Evaluating Cross-Subject and Cross-Device Consistency in Visual Fixation Prediction},
author = {Yuli Wu and Henning Konermann and Emil Mededovic and Peter Walter and Johannes Stegmaier},
journal= {arXiv preprint arXiv:2502.05554},
year = {2026}
}