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Therefore, this research aims at developing a method of extracting features from EEG signal for discriminating between ASD and control subjects. This study applies six prominent connectivity features, namely Cross Correlation (XCOR), Phase Locking Value (PLV), Pearson\u2019s Correlation Coefficient (PCC), Mutual Information (MI), Normalized Mutual Information (NMI) and Transfer Entropy (TE), for feature extraction. The Connectivity Feature Maps (CFMs) are constructed and used for classification through Convolutional Neural Network (CNN). As CFMs contain spatial information, they are able to distinguish ASD and control subjects better than other features. Rigorous experimentation has been performed on the EEG datasets collected from Italy and Saudi Arabia according to different criteria. MI feature shows the best result for categorizing ASD and control participants with increased sample size and segmentation. <\/jats:p>","DOI":"10.1142\/s012906572550011x","type":"journal-article","created":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T06:30:47Z","timestamp":1734157847000},"source":"Crossref","is-referenced-by-count":3,"title":["Autism Spectrum Disorder Detection Using Prominent Connectivity Features from Electroencephalography"],"prefix":"10.1142","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8361-0837","authenticated-orcid":false,"given":"Zahrul Jannat","family":"Peya","sequence":"first","affiliation":[{"name":"Computer Science and Engineering Department, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3600-0237","authenticated-orcid":false,"given":"Mahfuza Akter","family":"Maria","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1869-224X","authenticated-orcid":false,"given":"Sk Imran","family":"Hossain","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-8519","authenticated-orcid":false,"given":"M. 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