cvillanue/eye-tracking-ASD-Analysis
Analyzing Gaze Behavior of ASD and Neurotypical Participants: This repository utilizes deep learning models, including FNN and LSTM, for classifying participants based on gaze behavior patterns.
This project helps researchers and clinicians analyze eye-tracking data to identify subtle differences in gaze patterns between individuals with Autism Spectrum Disorder (ASD), neurotypical individuals, and those with unidentified classifications. It takes raw eye-tracking measurements from experiments, processes them, and then outputs classifications of participants based on their unique eye movement behaviors. The primary users are researchers in developmental psychology, neuroscience, or clinical psychology studying ASD.
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Use this if you have eye-tracking data for participants with and without ASD and want to automatically classify them or understand group differences in gaze behavior.
Not ideal if your primary goal is real-time gaze prediction or if you are working with non-eye-tracking physiological data.
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Apr 13, 2024
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