{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T22:59:00Z","timestamp":1778799540702,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"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":["61872004"],"award-info":[{"award-number":["61872004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072002"],"award-info":[{"award-number":["62072002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172004"],"award-info":[{"award-number":["62172004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAS-VPST Silk Road Science Fund 2021","award":["GLHZ202128"],"award-info":[{"award-number":["GLHZ202128"]}]},{"name":"Collaborative Innovation Program of Hefei Science Center"},{"name":"CAS","award":["2020HSC-CIP001"],"award-info":[{"award-number":["2020HSC-CIP001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1109\/jbhi.2022.3140531","type":"journal-article","created":{"date-parts":[[2022,1,5]],"date-time":"2022-01-05T20:29:47Z","timestamp":1641414587000},"page":"2559-2569","source":"Crossref","is-referenced-by-count":104,"title":["Transformer Model for Functional Near-Infrared Spectroscopy Classification"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0803-6206","authenticated-orcid":false,"given":"Zenghui","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5985-8023","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7541-0130","authenticated-orcid":false,"given":"Xiaochu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Psychology, School of Humanities and Social Science, University of Science and Technology of China, Hefei, China"}]},{"given":"Peng","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, School of Internet, and Institutes of Physical Science and Information Technology, Anhui University, Hefei, China"}]},{"given":"Bing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Technology, Maanshan, China"}]}],"member":"263","reference":[{"issue":"3","key":"ref1","first-page":"240","article-title":"Coupling of brain activity and cerebral blood flow: Basis of functional neuroimaging","volume":"7","author":"Villringer","year":"1995","journal-title":"Cerebrovascular Brain Metab. Rev."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.929199"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2015.00003"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1097\/00004647-199609000-00006"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.00040"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.03.028"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aaaf82"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref10","first-page":"5998","article-title":"Attention is all you need","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vaswani","year":"2017"},{"key":"ref11","first-page":"4171","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics, Hum. Lang. Technol.","author":"Devlin","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2959843"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00584"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102133"},{"key":"ref15","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref17","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","volume":"33","author":"Brown","year":"2020"},{"key":"ref18","article-title":"An image is worth 16X16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dosovitskiy","year":"2021"},{"key":"ref19","article-title":"Layer normalization","author":"Ba","year":"2016"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref21","first-page":"4604","article-title":"Do we need zero training loss after achieving zero training error","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"1","author":"Ishida","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-011-0792-5"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2010.03.013"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2014.6944008"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2016.2628057"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8121486"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/BF02447083"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.01.140"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2013.08.021"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s00221-013-3764-1"},{"key":"ref31","article-title":"Early convolutions help transformers see better","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","volume":"34","author":"Xiao","year":"2021"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref33","article-title":"Gaussian error linear units (GELUs)","author":"Hendrycks","year":"2016"},{"key":"ref34","article-title":"Adaptive input representations for neural language modeling","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Baevski","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2014.6944032"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICIEV.2016.7760042"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.23919\/ICCAS50221.2020.9268412"},{"key":"ref38","first-page":"4694","article-title":"When does label smoothing help","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","volume":"32","author":"Mller","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1117\/1.NPh.5.1.011008"},{"key":"ref40","article-title":"Decoupled weight decay regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Loshchilov","year":"2018"},{"key":"ref41","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Loshchilov","year":"2016"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2019.2938295"},{"issue":"11","key":"ref43","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2000.0613"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.10.025"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9788469\/09670659.pdf?arnumber=9670659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:16:03Z","timestamp":1705184163000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9670659\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":45,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2022.3140531","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6]]}}}