{"id":"https://openalex.org/W7161940185","doi":"https://doi.org/10.1109/access.2026.3695589","title":"HSST-EEG: A Hybrid State-Space and Transformer Architecture for EEG Decoding","display_name":"HSST-EEG: A Hybrid State-Space and Transformer Architecture for EEG Decoding","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7161940185","doi":"https://doi.org/10.1109/access.2026.3695589"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3695589","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3695589","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3695589","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136586890","display_name":"Emil Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Emil Kim","raw_affiliation_strings":["Department of Information Convergence Engineering, Pusan National University, Busan, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0001-5344-7549","affiliations":[{"raw_affiliation_string":"Department of Information Convergence Engineering, Pusan National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039893847","display_name":"Jin Kyu Gahm","orcid":"https://orcid.org/0000-0001-8827-402X"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin Kyu Gahm","raw_affiliation_strings":["School of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0001-8827-402X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4921948"],"apc_list":{"value":2075,"currency":"USD","value_usd":2075},"apc_paid":{"value":2075,"currency":"USD","value_usd":2075},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.54644555,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"79188","last_page":"79196"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.7940000295639038,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.7940000295639038,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11021","display_name":"ECG Monitoring and Analysis","score":0.04149999842047691,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.03889999911189079,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.7170000076293945},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5863000154495239},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.487199991941452},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4659000039100647},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.29350000619888306}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7858999967575073},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.7170000076293945},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5863000154495239},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.487199991941452},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4659000039100647},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4090999960899353},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3314000070095062},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C193969084","wikidata":"https://www.wikidata.org/wiki/Q7452500","display_name":"Sequential decoding","level":4,"score":0.28110000491142273},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.27799999713897705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C2994168587","wikidata":"https://www.wikidata.org/wiki/Q5295","display_name":"Random access memory","level":2,"score":0.26339998841285706}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3695589","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3695589","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:4b6e98c49d3f4f4f8caa2a512e9547ae","is_oa":true,"landing_page_url":"https://doaj.org/article/4b6e98c49d3f4f4f8caa2a512e9547ae","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 14, Pp 79188-79196 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3695589","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3695589","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.45332732796669006,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"interpretation":[1],"of":[2,9],"electroencephalography":[3],"(EEG)":[4],"signals":[5],"requires":[6],"effective":[7],"modeling":[8,86,102,147],"high-dimensional,":[10],"non-stationary,":[11],"and":[12,24,64,77,155,172],"long-range":[13,61,84],"spatio-temporal":[14,118],"dynamics,":[15],"which":[16,110],"remains":[17],"a":[18,140,184],"central":[19],"challenge":[20],"in":[21,55,187],"both":[22],"clinical":[23,164],"BCI-oriented":[25],"decoding":[26,48,194],"tasks.":[27],"Recent":[28],"approaches":[29],"that":[30,143,179],"combine":[31],"convolutional":[32],"neural":[33],"networks":[34],"(CNNs)":[35],"or":[36],"explicit":[37],"spectral":[38],"feature":[39],"extractors":[40],"with":[41,148],"downstream":[42],"sequence":[43],"models":[44],"have":[45],"improved":[46],"EEG":[47,119,138,165,170,175,193],"performance.":[49],"However,":[50],"such":[51],"pipelines":[52],"remain":[53],"limited":[54],"their":[56,113],"ability":[57],"to":[58,115,122,151,183,191],"jointly":[59],"capture":[60,74],"temporal":[62,76,103],"dependencies":[63,104],"dynamic":[65],"cross-channel":[66],"interactions":[67],"while":[68],"maintaining":[69],"computational":[70,90,153],"efficiency.":[71],"Transformer-based":[72,149],"architectures":[73,96],"pairwise":[75],"spatial":[78],"features":[79],"via":[80],"self-attention,":[81],"enabling":[82],"flexible":[83],"dependency":[85],"but":[87],"incurring":[88],"quadratic":[89],"complexity.":[91],"In":[92],"contrast,":[93],"Mamba-based":[94,145],"state-space":[95,146],"achieve":[97],"favorable":[98],"linear-time":[99],"complexity":[100],"by":[101],"implicitly":[105],"through":[106],"shared":[107],"state":[108],"evolution,":[109],"may":[111],"limit":[112],"capacity":[114],"represent":[116],"complex":[117],"structures":[120],"compared":[121,190],"similarly":[123],"sized":[124],"Transformer":[125,136],"models.":[126],"To":[127],"address":[128],"these":[129],"limitations,":[130],"we":[131],"propose":[132],"HSST-EEG":[133,180],"(Hybrid":[134],"State-Space":[135],"for":[137],"Decoding),":[139],"hybrid":[141],"architecture":[142],"integrates":[144],"attention":[150],"balance":[152],"efficiency":[154],"representational":[156],"capacity.":[157],"Experiments":[158],"conducted":[159],"on":[160],"two":[161],"widely":[162],"used":[163],"benchmarks,":[166],"the":[167,173],"CHB-MIT":[168],"Scalp":[169],"Database":[171],"TUH":[174],"Events":[176],"Corpus,":[177],"demonstrate":[178],"achieves":[181],"up":[182],"5%":[185],"improvement":[186],"classification":[188],"accuracy":[189],"recent":[192],"baselines.":[195]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-05-22T00:00:00"}