{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:05:20Z","timestamp":1780434320932,"version":"3.54.1"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Institute of Information & communications Technology Planning & Evaluation"},{"name":"Korean Government","award":["MSIT; IITP-2026-RS-2023-00254177"],"award-info":[{"award-number":["MSIT; IITP-2026-RS-2023-00254177"]}]},{"name":"Korean Government","award":["IITP-2026-RS-2024-00360227"],"award-info":[{"award-number":["IITP-2026-RS-2024-00360227"]}]},{"name":"Korean Government","award":["IITP-2026-RS-2025-02219116"],"award-info":[{"award-number":["IITP-2026-RS-2025-02219116"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3695589","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T19:44:00Z","timestamp":1779392640000},"page":"79188-79196","source":"Crossref","is-referenced-by-count":0,"title":["HSST-EEG: A Hybrid State-Space and Transformer Architecture for EEG Decoding"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5344-7549","authenticated-orcid":false,"given":"Emil","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Information Convergence Engineering, Pusan National University, Busan, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8827-402X","authenticated-orcid":false,"given":"Jin Kyu","family":"Gahm","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tins.2017.02.004"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aace8c"},{"key":"ref3","article-title":"Application of machine learning to epileptic seizure onset detection and treatment","author":"Shoeb","year":"2009"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/spmb.2015.7405421"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-15052-2"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/access.2025.3578991"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23730"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/taffc.2022.3169001"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2022.3230250"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102598"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tcds.2020.3012278"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tnsre.2024.3355750"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2022.3197419"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-71118-7"},{"key":"ref16","first-page":"78240","article-title":"BIOT: Biosignal transformer for cross-data learning in the wild","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref17","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu","year":"2024","journal-title":"arXiv:2312.00752"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-96-0901-7_14"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/BioCAS67066.2025.00057"},{"key":"ref20","article-title":"Jamba: A hybrid transformer-mamba language model","author":"Lieber","year":"2024","journal-title":"arXiv:2403.19887"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671451"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2026.3664424"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tassp.1984.1164317"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07992-w"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1958.tb03874.x"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref27","article-title":"Vision Mamba: Efficient visual representation learning with bidirectional state space model","author":"Zhu","year":"2024","journal-title":"arXiv:2401.09417"},{"key":"ref28","article-title":"Layer normalization","author":"Ba","year":"2016","journal-title":"arXiv:1607.06450"},{"issue":"1","key":"ref29","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref30","article-title":"Network in network","author":"Lin","year":"2013","journal-title":"arXiv:1312.4400"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1212\/wnl.0000000000207127"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11533369.pdf?arnumber=11533369","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:08:10Z","timestamp":1780430890000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11533369\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3695589","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}