Characterising Eye Movement Events With Multi-Scale Spatio-Temporal Awareness

The intricate and dynamic nature of eye movements serves as a window into the realms of cognition, emotion, and physiological responses. Event detection, in turn, is instrumental in the precise recognition and categorization of these diverse eye movements. Deep learning methods have recently been applied to event detection, yielding promising results. However, the intrinsic multi-scale attributes of events have often been overlooked in existing approaches. To address this, we introduce “GazeUNet”, a novel network based on U-Net and Bi-GRU, which classifies gaze samples into three categories: fixation, saccade, and post-saccadic oscillations. Firstly, multi-scale spatial features are captured using a U-Net model, and then a hierarchical bidirectional gated recurrent unit (Bi-GRU) is employed to extract temporal correlations, followed by classification through fully connected layers. Our results, derived from the analysis of three publicly available datasets, consistently showcase the superiority of the proposed model compared with other state-of-the-art methods across all categories.