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

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.Clinical relevance-Cross-device and cross-subject consistency in visual fixations can improve visual prosthetics for individuals with visual impairments by enabling reliable eye-gaze-assisted functionality.