Dominant Cross-Frequency Analysis for Early Autism Diagnosis in Young Children Using Deep Learning and EEG Signals

This study proposes a machine learning approach combining fuzzy inference systems and convolutional neural networks to detect autism spectrum disorder in children under 2 years old using electroencephalogram (EEG) signals. The method analyzes cross-frequency interactions in brain activity and reported 99.03% classification accuracy compared to traditional approaches.