USC’s Laurent Itti and researchers from Queen’s University in Ontario have created a data heavy, low cost method of identifying brain disorders through eye tracking. Subjects watch a video for 15 minutes while their eye movements are recorded. An enormous amount of data is generated as the average person makes three to five saccadic eye movements per second. Itti’s team uses advanced machine learning algorithms to enable a computer to recognize patterns without explicit human instruction.
The proof of concept study found that the algorithm could classify mental disorders through eye movement patterns. Parkinson’s patients were identified with nearly 90 percent accuracy. Children with ADHD or fetal alcohol spectrum disorder were identified with 77 percent accuracy. “This is very different from what people have done before. We’re trying to have completely automated interpretation of the eye movement data,” said Itti.