Identification of autism spectrum disorder using electroencephalography and machine learning: a review

This review examines the integration of electroencephalography (EEG), machine learning, and statistical analysis as tools for improving the early identification of autism spectrum disorder. The paper compares EEG datasets, preprocessing techniques, feature extraction methods, and classification models while identifying research gaps and future directions in ASD diagnosis.