Adrien E. E. Dubois

The paper analyzes EEG data from 4,812 individuals to identify shared and distinct neurophysiological signatures across psychiatric conditions and rare genetic variants.

This study uses self-supervised learning to classify autism spectrum condition from hyperscanning EEG data during social interactions, achieving 78.13% accuracy.

The paper analyzes EEG data from a large cohort to show aperiodic neural activity decreases monotonically with age, while periodic activity increases then decreases, mapping to distinct biological processes.