Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD

Abstract

This study provided a novel method for detecting personalized spatial-frequency abnormalities of children with ADHD at a precise spatial-frequency resolution. We proposed a new form of representation of multichannel EEG data that is compatible with mainstream CNN architectures. We ensured that CNN models were interpretable and reliable relating to clinical practice by visualizing the decision-making process. We expect that detection of personalized abnormalities using deep learning techniques can facilitate the identification of potential neural pathways and the planning of targeted treatments for children with ADHD.