{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T10:38:37Z","timestamp":1776163117544,"version":"3.50.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62222603"],"award-info":[{"award-number":["62222603"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"STI2030-Major Projects"},{"name":"Ministry of Science and Technology of the People","award":["2021ZD0200700"],"award-info":[{"award-number":["2021ZD0200700"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2023B0303030001"],"award-info":[{"award-number":["2023B0303030001"]}]},{"name":"Program for Guangdong Introducing Innovative and Entrepreneurial Teams","award":["2019ZT08X214"],"award-info":[{"award-number":["2019ZT08X214"]}]},{"name":"Science and Technology Program of Guangzhou","award":["2024A04J6310"],"award-info":[{"award-number":["2024A04J6310"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Affective Comput."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1109\/taffc.2025.3527459","type":"journal-article","created":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T15:17:35Z","timestamp":1736349455000},"page":"1592-1605","source":"Crossref","is-referenced-by-count":9,"title":["TFAGL: A Novel Agent Graph Learning Method Using Time-Frequency EEG for Major Depressive Disorder Detection"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9171-5608","authenticated-orcid":false,"given":"Zihua","family":"Xu","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5451-7230","authenticated-orcid":false,"given":"C. L. Philip","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7025-6365","authenticated-orcid":false,"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information, School of Computer Science and Engineering, South China University of Technology, Guangzhou, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(11)60602-8"},{"key":"ref2","article-title":"World mental health report: Transforming mental health for all","year":"2022"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eplepsyres.2022.106868"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3476778"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-05507-6"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2668"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.07.004"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2971656"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.02.015"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2927121"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11920-012-0322-7"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1111\/cns.12835"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/184"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3072345"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.4249\/scholarpedia.4720"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126262"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2022.3210958"},{"key":"ref18","article-title":"Learning topology-agnostic EEG representations with geometry-aware modeling","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yi"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47606-8_35"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2021.109209"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2021.3139104"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3291950"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2894423"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2020.3043426"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-018-0292-5"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2019.8929254"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.4103\/0253-7176.106011"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3053999"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2024.3349770"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2024.3371540"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2022.3197127"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad038b"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122356"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM58861.2023.10385659"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3225855"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2024.3385676"},{"key":"ref38","article-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3059429"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cobeha.2017.09.005"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-022-01927-9"},{"key":"ref42","first-page":"2420","article-title":"Consistent feature selection for analytic deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dinh"},{"issue":"177","key":"ref43","first-page":"1","article-title":"All models are wrong, but many are useful: Learning a variable\u2019s importance by studying an entire class of prediction models simultaneously","volume":"20","author":"Fisher","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02005"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10448044"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01211-x"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1162\/CPSY_a_00024"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01409-z"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1159\/000438457"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IC_ASET49463.2020.9318302"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669572"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3110665"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3045718"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110190"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12050630"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.09.031"},{"key":"ref58","first-page":"7264","article-title":"Convolutional neural networks on graphs with chebyshev approximation, revisited","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/NER.2013.6695876"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3351177"}],"container-title":["IEEE Transactions on Affective Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5165369\/11152495\/10834580.pdf?arnumber=10834580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T05:30:50Z","timestamp":1757136650000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10834580\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":60,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/taffc.2025.3527459","relation":{},"ISSN":["1949-3045","2371-9850"],"issn-type":[{"value":"1949-3045","type":"electronic"},{"value":"2371-9850","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]}}}