Deep learning for diagnosing autism spectrum disorder using EEG with upright and inverted face processing tasks | Synapse
March 3, 2026
Deep learning for diagnosing autism spectrum disorder using EEG with upright and inverted face processing tasks
Key Points
Deep learning models achieve high accuracy in diagnosing autism spectrum disorder using EEG data, particularly during face processing tasks.
The analysis compares performance of upright and inverted face processing, highlighting distinct patterns associated with autism.
Data was collected from EEG recordings during face processing tasks, revealing significant neural differences in individuals with autism.
These findings suggest that deep learning techniques may enhance diagnostic accuracy for autism spectrum disorder, indicating future research directions.