{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:08:38Z","timestamp":1783436918670,"version":"3.54.6"},"reference-count":52,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"STI2030-Major Projects","award":["2022ZD0208805"],"award-info":[{"award-number":["2022ZD0208805"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["623B2085"],"award-info":[{"award-number":["623B2085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Pioneer and Leading Goose R&amp;D Program of Zhejiang","award":["2024C03002"],"award-info":[{"award-number":["2024C03002"]}]},{"name":"Key Project of Westlake Institute for Optoelectronics","award":["2023GD004"],"award-info":[{"award-number":["2023GD004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/jbhi.2024.3415959","type":"journal-article","created":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T13:55:21Z","timestamp":1718718921000},"page":"970-980","source":"Crossref","is-referenced-by-count":9,"title":["Neuro-BERT: Rethinking Masked Autoencoding for Self-Supervised Neurological Pretraining"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6589-7136","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6806-2468","authenticated-orcid":false,"given":"Siyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Engineering, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4148-0042","authenticated-orcid":false,"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4137-7272","authenticated-orcid":false,"given":"Mohamad","family":"Sawan","sequence":"additional","affiliation":[{"name":"Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech), School of Engineering, Westlake University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2872675"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979670"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2887223"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101765"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3382507.3418845"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICORR.2005.1501122"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ac73b3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2023.3235390"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref10","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gidaris","year":"2018"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref13","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"Caron","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"ref14","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.08.001"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/324"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9414752"},{"key":"ref21","first-page":"268","article-title":"Exploring localization for self-supervised fine-grained contrastive learning","volume-title":"Proc. BMVC","author":"Wu","year":"2022"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"ref26","first-page":"12310","article-title":"Barlow TWINS: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar","year":"2021"},{"key":"ref27","first-page":"2022","article-title":"Cost: Contrastive learning of disentangled seasonal-trend representations for time series forecasting","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Woo"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abca18"},{"key":"ref29","article-title":"BEiT: BERT pre-training of image transformers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bao","year":"2022"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref31","first-page":"3-10","article-title":"Autoencoders, minimum description length and Helmholtz free energy","volume-title":"Proc. 6th Int. Conf. Neural Inf. Process. Syst.","author":"Hinton","year":"1993"},{"issue":"12","key":"ref32","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref34","first-page":"1691","article-title":"Generative pretraining from pixels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen","year":"2020"},{"key":"ref35","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"ICLR"},{"key":"ref36","article-title":"Bridging Nonlinearities and Stochastic Regularizers With Gaussian Error Linear Units","author":"Hendrycks","year":"2016"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475697"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2003.09.014"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2006.03.010"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1111\/psyp.12283"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/S1567-4231(04)04006-7"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.061907"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2021.3076234"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0186132"},{"key":"ref48","first-page":"126","article-title":"SEMG gesture recognition with a simple model of attention","volume-title":"Proc. Mach. Learn. Health","author":"Josephs","year":"2020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00649"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/130385.130401"},{"key":"ref51","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Paszke","year":"2019"},{"key":"ref52","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11372623\/10561479.pdf?arnumber=10561479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:07:33Z","timestamp":1770671253000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10561479\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":52,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2024.3415959","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}