Refining ADHD diagnosis with EEG: The impact of preprocessing and temporal segmentation on classification accuracy.

Background: EEG signals are commonly used in ADHD diagnosis, but they are often affected by noise and artifacts. Effective preprocessing and segmentation methods can significantly enhance the accuracy and reliability of ADHD classification.

Methods: We applied filtering, ASR, and ICA preprocessing techniques to EEG data from children with ADHD and neurotypical controls.

A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning.

Objective: Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field.