{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:10:09Z","timestamp":1778605809411,"version":"3.51.4"},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers and Electrical Engineering"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.compeleceng.2026.111190","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T22:24:59Z","timestamp":1777069499000},"page":"111190","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Electroencephalography-based classification of individuals with attention deficit hyperactivity disorder using brain connectivity information and deep learning"],"prefix":"10.1016","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8650-1137","authenticated-orcid":false,"given":"Ozlem","family":"Karabiber Cura","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6182-7173","authenticated-orcid":false,"given":"Fatma Gunseli","family":"Yasar Ciklacandir","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compeleceng.2026.111190_b1","series-title":"Burden of ADHD","year":"2024"},{"key":"10.1016\/j.compeleceng.2026.111190_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106676","article-title":"An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals","volume":"155","author":"Khare","year":"2023","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.compeleceng.2026.111190_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.105941","article-title":"Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with eeg signals","volume":"200","author":"Tor","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.compeleceng.2026.111190_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119219","article-title":"Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study","volume":"213","author":"Sharma","year":"2023","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.compeleceng.2026.111190_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.clinph.2023.01.021","article-title":"The role of single trial variability in event related potentials in children with attention deficit hyperactivity disorder","volume":"149","author":"Arnett","year":"2023","journal-title":"Clin Neurophysiol"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b6","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1016\/j.bbe.2020.04.006","article-title":"Diagnosis of attention deficit hyperactivity disorder with combined time and frequency features","volume":"40","author":"Alt\u0131nkaynak","year":"2020","journal-title":"Biocybern Biomed Eng"},{"issue":"7","key":"10.1016\/j.compeleceng.2026.111190_b7","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.3390\/jcm8071055","article-title":"Deep learning based on event-related EEG differentiates children with ADHD from healthy controls","volume":"8","author":"Vahid","year":"2019","journal-title":"J Clin Med"},{"key":"10.1016\/j.compeleceng.2026.111190_b8","doi-asserted-by":"crossref","first-page":"251","DOI":"10.3389\/fnins.2020.00251","article-title":"Deep learning convolutional neural networks discriminate adult adhd from healthy individuals on the basis of event-related spectral EEG","volume":"14","author":"Dubreuil-Vall","year":"2020","journal-title":"Front Neurosci"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b9","article-title":"Efficient feature selection and machine learning based ADHD detection using EEG signal","volume":"72","author":"Maniruzzaman","year":"2022","journal-title":"Comput Mater Contin"},{"key":"10.1016\/j.compeleceng.2026.111190_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108277","article-title":"Leveraging brain\u2013computer interface for implementation of a bio-sensor controlled game for attention deficit people","volume":"102","author":"Amin","year":"2022","journal-title":"Comput Electr Eng"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.111190_b11","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1049\/iet-syb.2018.5130","article-title":"Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal","volume":"13","author":"Boroujeni","year":"2019","journal-title":"IET Syst Biol"},{"key":"10.1016\/j.compeleceng.2026.111190_b12","article-title":"Attention deficit hyperactivity disorder recognition based on intrinsic time-scale decomposition of EEG signals","volume":"81","author":"Cura","year":"2023","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b13","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/acc902","article-title":"Detection of ADHD from EEG signals using new hybrid decomposition and deep learning techniques","volume":"20","author":"Esas","year":"2023","journal-title":"J Neural Eng"},{"key":"10.1016\/j.compeleceng.2026.111190_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105791","article-title":"Investigating the discrimination of linear and nonlinear effective connectivity patterns of EEG signals in children with attention-deficit\/hyperactivity disorder and typically developing children","volume":"148","author":"Talebi","year":"2022","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.compeleceng.2026.111190_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.brainres.2020.147142","article-title":"Investigating brain electrical activity and functional connectivity in adolescents with clinically elevated levels of ADHD symptoms in alpha frequency band","volume":"1750","author":"Debnath","year":"2021","journal-title":"Brain Res"},{"key":"10.1016\/j.compeleceng.2026.111190_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111468","article-title":"New interdependence feature of EEG signals as a biomarker of timing deficits evaluated in attention-deficit\/hyperactivity disorder detection","volume":"199","author":"Ghaderyan","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.compeleceng.2026.111190_b17","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2019.04.058","article-title":"A deep learning framework for identifying children with ADHD using an EEG-based brain network","volume":"356","author":"Chen","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.compeleceng.2026.111190_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104515","article-title":"Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD","volume":"134","author":"Abbas","year":"2021","journal-title":"Comput Biol Med"},{"issue":"2","key":"10.1016\/j.compeleceng.2026.111190_b19","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.neuroimage.2011.04.070","article-title":"Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology","volume":"58","author":"Ahmadlou","year":"2011","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.compeleceng.2026.111190_b20","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1016\/j.clinph.2015.09.134","article-title":"The variability of EEG functional connectivity of young ADHD subjects in different resting states","volume":"127","author":"Alba","year":"2016","journal-title":"Clin Neurophysiol"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b21","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1016\/j.clinph.2019.08.010","article-title":"Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms","volume":"131","author":"Kiiski","year":"2020","journal-title":"Clin Neurophysiol"},{"key":"10.1016\/j.compeleceng.2026.111190_b22","doi-asserted-by":"crossref","DOI":"10.18502\/fbt.v8i2.6515","article-title":"Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals","author":"Ekhlasi","year":"2021","journal-title":"Front Biomed Technol"},{"key":"10.1016\/j.compeleceng.2026.111190_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103708","article-title":"ADHD detection using dynamic connectivity patterns of EEG data and ConvLSTM with attention framework","volume":"76","author":"Bakhtyari","year":"2022","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"10.1016\/j.compeleceng.2026.111190_b24","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s12021-024-09685-3","article-title":"Improved ADHD diagnosis using EEG connectivity and deep learning through combining pearson correlation coefficient and phase-locking value","volume":"22","author":"Ahmadi Moghadam","year":"2024","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.compeleceng.2026.111190_b25","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105738","article-title":"Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG","volume":"197","author":"Moghaddari","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.compeleceng.2026.111190_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102227","article-title":"Computer aided diagnosis system using deep convolutional neural networks for ADHD subtypes","volume":"63","author":"Ahmadi","year":"2021","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b27","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1007\/s13755-024-00305-7","article-title":"Convolutional neural network framework for EEG-based ADHD diagnosis in children","volume":"12","author":"Hassan","year":"2024","journal-title":"Health Inf Sci Syst"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b28","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/brainsci15010095","article-title":"EEG-based ADHD classification using autoencoder feature extraction and resnet with double augmented attention mechanism","volume":"15","author":"Bansal","year":"2025","journal-title":"Brain Sci"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b29","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1007\/s13246-021-01018-x","article-title":"Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning","volume":"44","author":"Tosun","year":"2021","journal-title":"Phys Eng Sci Med"},{"issue":"02","key":"10.1016\/j.compeleceng.2026.111190_b30","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065716500398","article-title":"Deep learning representation from electroencephalography of early-stage Creutzfeldt-Jakob disease and features for differentiation from rapidly progressive dementia","volume":"27","author":"Morabito","year":"2017","journal-title":"Int J Neural Syst"},{"issue":"22","key":"10.1016\/j.compeleceng.2026.111190_b31","doi-asserted-by":"crossref","first-page":"4698","DOI":"10.3390\/math11224698","article-title":"Developing system-based artificial intelligence models for detecting the attention deficit hyperactivity disorder","volume":"11","author":"Alkahtani","year":"2023","journal-title":"Mathematics"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.111190_b32","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1007\/s12559-024-10302-3","article-title":"Efficient deep learning approach for diagnosis of attention-deficit\/hyperactivity disorder in children based on EEG signals","volume":"16","author":"Jahani","year":"2024","journal-title":"Cogn Comput"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b33","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1016\/j.bbe.2024.07.003","article-title":"Detection of attention deficit hyperactivity disorder based on EEG feature maps and deep learning","volume":"44","author":"Cura","year":"2024","journal-title":"Biocybern Biomed Eng"},{"key":"10.1016\/j.compeleceng.2026.111190_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109092","article-title":"Siamese based deep neural network for ADHD detection using EEG signal","volume":"182","author":"Latifi","year":"2024","journal-title":"Comput Biol Med"},{"issue":"13","key":"10.1016\/j.compeleceng.2026.111190_b35","doi-asserted-by":"crossref","first-page":"4747","DOI":"10.3390\/s22134747","article-title":"A novel approach for segment-length selection based on stationarity to perform effective connectivity analysis applied to resting-state eeg signals","volume":"22","author":"G\u00f3ngora","year":"2022","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.compeleceng.2026.111190_b36","doi-asserted-by":"crossref","first-page":"2932","DOI":"10.1016\/j.jfranklin.2018.01.014","article-title":"Measures of generalized magnitude-squared coherence: Differences and similarities","volume":"355","author":"Malekpour","year":"2018","journal-title":"J Franklin Inst"},{"key":"10.1016\/j.compeleceng.2026.111190_b37","doi-asserted-by":"crossref","first-page":"17652","DOI":"10.1109\/ACCESS.2020.2967814","article-title":"Common cross-spectral patterns of electroencephalography for reliable cognitive task identification","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b38","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.neuroimage.2009.06.056","article-title":"A comparative study of synchrony measures for the early diagnosis of Alzheimer\u2019s disease based on EEG","volume":"49","author":"Dauwels","year":"2010","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b39","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.bbe.2020.01.013","article-title":"Sleep EEG analysis utilizing inter-channel covariance matrices","volume":"40","author":"Prabhu","year":"2020","journal-title":"Biocybern Biomed Eng"},{"issue":"1","key":"10.1016\/j.compeleceng.2026.111190_b40","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.sigpro.2008.07.005","article-title":"Correntropy as a novel measure for nonlinearity tests","volume":"89","author":"Gunduz","year":"2009","journal-title":"Signal Process"},{"key":"10.1016\/j.compeleceng.2026.111190_b41","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.compeleceng.2026.111190_b42","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.compeleceng.2026.111190_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109999","article-title":"An explainable liquid neural network combined with path aggregation residual network for an accurate brain tumor diagnosis","volume":"122","author":"Shaheema","year":"2025","journal-title":"Comput Electr Eng"},{"key":"10.1016\/j.compeleceng.2026.111190_b44","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"3","key":"10.1016\/j.compeleceng.2026.111190_b45","first-page":"359","article-title":"Discrimination of ADHD subtypes using decision tree on behavioral, neuropsychological, and neural markers","volume":"11","author":"Rostami","year":"2020","journal-title":"Basic Clin Neurosci"},{"issue":"11","key":"10.1016\/j.compeleceng.2026.111190_b46","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1177\/1087054716649666","article-title":"Diagnostic classification of ADHD versus control: support vector machine classification using brief neuropsychological assessment","volume":"24","author":"Bledsoe","year":"2020","journal-title":"J Atten Disord"},{"key":"10.1016\/j.compeleceng.2026.111190_b47","first-page":"1","article-title":"Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset","author":"Ahire","year":"2023","journal-title":"Appl Neuropsychol: Child"},{"key":"10.1016\/j.compeleceng.2026.111190_b48","article-title":"Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques","volume":"26","author":"Luo","year":"2020","journal-title":"NeuroImage: Clin"},{"key":"10.1016\/j.compeleceng.2026.111190_b49","series-title":"EEG data for ADHD \/ control children","author":"Motie Nasrabadi","year":"2020"},{"key":"10.1016\/j.compeleceng.2026.111190_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2026.110962","article-title":"Quantitative EEG-based autism spectrum disorder detection using neural sequence models","volume":"131","author":"Nour","year":"2026","journal-title":"Comput Electr Eng"}],"container-title":["Computers and Electrical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626002624?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0045790626002624?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:27:38Z","timestamp":1778603258000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0045790626002624"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":50,"alternative-id":["S0045790626002624"],"URL":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111190","relation":{},"ISSN":["0045-7906"],"issn-type":[{"value":"0045-7906","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Electroencephalography-based classification of individuals with attention deficit hyperactivity disorder using brain connectivity information and deep learning","name":"articletitle","label":"Article Title"},{"value":"Computers and Electrical Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compeleceng.2026.111190","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111190"}}