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In this study, the phase-based functional brain connectivity derived from electroencephalogram (EEG) in a machine learning framework was used to classify the children with autism and typical children in an experimentally obtained data set of 12 autism spectrum disorder (ASD) and 12 typical children. Specifically, the functional brain connectivity networks have quantitatively been characterized by graph-theoretic parameters computed from three proposed approaches based on a standard phase-locking value, which were used as the features in a machine learning environment. Our study was successfully classified between two groups with approximately 95.8% accuracy, 100% sensitivity, and 92% specificity through the trial-averaged phase-locking value (PLV) approach and cubic support vector machine (SVM). This work has also shown that significant changes in functional brain connectivity in ASD children have been revealed at theta band using the aggregated graph-theoretic features. Therefore, the findings from this study offer insight into the potential use of functional brain connectivity as a tool for classifying ASD children.<\/jats:p>","DOI":"10.1162\/neco_a_01394","type":"journal-article","created":{"date-parts":[[2021,4,22]],"date-time":"2021-04-22T23:22:25Z","timestamp":1619133745000},"page":"1914-1941","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":56,"title":["Classification of Autism Spectrum Disorder From EEG-Based Functional Brain Connectivity Analysis"],"prefix":"10.1162","volume":"33","author":[{"given":"Noura","family":"Alotaibi","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK nma1y17@soton.ac.uk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koushik","family":"Maharatna","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer 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