Eye movements and the role of the quick phase

By examining eye fixations, we assess the complexity of interpreting the fixated information and the proportion of fixation time per question, thereby illuminating cognitive processing mechanisms. Transitioning to the analysis of Nystagmus from eye-tracking data, this is crucial for the clinical interpretation of this pathological movement. However, automating this analysis is challenging due to the presence of natural eye movements and blink artifacts, which interfere with the signal of interest. To address this, we propose a Convolutional Dictionary Learning-based method that automatically identifies Nystagmus waveforms, effectively distinguishing natural eye movements from pathological ones. Our approach shows enhanced pattern recovery rates, as demonstrated by clinical examples that illustrate its effectiveness.