Eye Movement

Eye movement research investigates the relationship between eye movements and cognitive processes, aiming to understand how gaze patterns reflect attention, comprehension, and other mental states. Current research heavily utilizes machine learning, particularly deep learning models like Transformers, LSTMs, and convolutional neural networks, to analyze large-scale eye-tracking datasets and predict cognitive states from gaze patterns, often incorporating multimodal data such as EEG and visual stimuli. This work has implications for diagnosing neurodegenerative diseases like Parkinson's and Alzheimer's, improving human-computer interaction, and advancing our understanding of reading and dyslexia.