ADHD Diagnosis in Children Using EEG Amplitude Modulation Features and Machine Learning
This study proposes a diagnostic framework for childhood ADHD by extracting amplitude modulation features from EEG signals decomposed via wavelet analysis, achieving 99.46% accuracy with a KNN classifier. The approach integrates conventional and novel AM features, with discriminative information concentrated in gamma/beta bands and frontal brain regions, outperforming previous methods.