{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,14]],"date-time":"2026-11-14T20:13:43Z","timestamp":1794687223259,"version":"4.9.1"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Alzheimer\u2019s Research U.K.","award":["ARUK-PPG20114B-25"],"award-info":[{"award-number":["ARUK-PPG20114B-25"]}]},{"DOI":"10.13039\/501100001451","name":"Agency for Science, Technology and Research (A*STAR), Artificial Intelligence, Analytics and Informatics (AI3), Horizontal Technology Programme Office","doi-asserted-by":"publisher","award":["C211118015"],"award-info":[{"award-number":["C211118015"]}],"id":[{"id":"10.13039\/501100001451","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Syst. Rehabil. Eng."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tnsre.2022.3204913","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T15:27:37Z","timestamp":1662478057000},"page":"2651-2660","source":"Crossref","is-referenced-by-count":150,"title":["EEG-Based Graph Neural Network Classification of Alzheimer\u2019s Disease: An Empirical Evaluation of Functional Connectivity Methods"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7584-9074","authenticated-orcid":false,"given":"Dominik","family":"Klepl","sequence":"first","affiliation":[{"name":"Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9176-6674","authenticated-orcid":false,"given":"Fei","family":"He","sequence":"additional","affiliation":[{"name":"Centre for Computational Science and Mathematical Modelling, Coventry University, Coventry, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-3600","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, A&#x002A;STAR, Connexis North Tower, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8886-1283","authenticated-orcid":false,"given":"Daniel J.","family":"Blackburn","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, The University of Sheffield, Sheffield, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ptolemaios","family":"Sarrigiannis","sequence":"additional","affiliation":[{"name":"Department of Neurophysiology, Royal Devon and Exeter NHS Foundation Trust, Exeter, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00192"},{"key":"ref32","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1957.tb01515.x"},{"key":"ref37","first-page":"1","article-title":"Fast graph representation learning with PyTorch geometric","author":"fey","year":"2019","journal-title":"Proc ICLR Workshop Represent Learn Graphs Manifolds"},{"key":"ref36","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"paszke","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","author":"kuhn","year":"2021","journal-title":"Caret Classification and Regression Training"},{"key":"ref34","year":"2021","journal-title":"R A Language and Environment for Statistical Computing"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2015.02.008"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000100"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaaa76"},{"key":"ref13","article-title":"Decreased coherence and functional connectivity of electroencephalograph in Alzheimer&#x2019;s disease","volume":"24","author":"wang","year":"2014","journal-title":"Chaos Interdiscipl J Nonlinear Sci"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-210394"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-020-00632-3"},{"key":"ref16","doi-asserted-by":"crossref","DOI":"10.1088\/1741-2552\/ab4024","article-title":"Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer&#x2019;s disease continuum","volume":"16","author":"ruiz-g\u00f3mez","year":"2019","journal-title":"J Neural Eng"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhj127"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2004.04.029"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2016.08.013"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.01.055"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1089\/brain.2014.0236"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.20346"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2011.11.055"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3105397"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2017.00028"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci8070134"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.06.056"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/5174815"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023832305702"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s11062-015-9496-z"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)61349-9"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2020.00255"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1088\/2632-072X\/ac5f8d"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2019.07.016"},{"key":"ref24","first-page":"367","article-title":"EEG-GCNN: Augmenting electroencephalogram-based neurological disease diagnosis using a domain-guided graph convolutional neural network","author":"wagh","year":"2020","journal-title":"Proc Mach Learn Health"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9630194"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.2994159"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2021.10.023"}],"container-title":["IEEE Transactions on Neural Systems and Rehabilitation Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7333\/9695946\/09878348.pdf?arnumber=9878348","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T16:15:26Z","timestamp":1665418526000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9878348\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/tnsre.2022.3204913","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.06.14.496080","asserted-by":"object"}],"has-part":[{"id-type":"doi","id":"https:\/\/doi.org\/10.1109\/tnsre.2022.3204913\/mm1","asserted-by":"subject"}]},"ISSN":["1534-4320","1558-0210"],"issn-type":[{"value":"1534-4320","type":"print"},{"value":"1558-0210","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}