Physiological Data Augmentation for Eye Movement Gaze in Deep Learning

Abstract

In this study, the challenges posed by limited annotated medical data in the field of eye movement AI analysis are addressed through the introduction of a novel physiologically based gaze data augmentation library. Unlike traditional augmentation methods, which may introduce artifacts and alter pathological features in medical datasets, the proposed library emulates natural head movements during gaze data collection. This approach enhances sample diversity without compromising authenticity. The library evaluation was conducted on both CNN and hybrid architectures using distinct datasets, demonstrating its effectiveness in regularizing the training process and improving generalization. What is particularly noteworthy is the achievement of a macro F1 score of up to 79% when trained using the proposed augmentation (EMULATE) with the three HTCE variants. This pioneering approach leverages domain-specific knowledge to contribute to the robustness and authenticity of deep learning models in the medical domain.