Temporal sampling of visual processing oscillations in adults with ADHD

This dataset explores temporal differences in visual processing between adults with ADHD and neurotypical individuals. Collected during a visual word recognition task, the data demonstrate that a machine learning algorithm can classify participants according to the group they belong to (ADHD vs control) with 91.8% accuracy and distinguish psychostimulant medication users within the ADHD group with 91.3% accuracy.

These results reveal behavioral markers of ADHD and medication use, detected through random temporal sampling. This dataset contributes to a better understanding of the impact of ADHD on perceptual oscillations.