{"id":"https://openalex.org/W4415251289","doi":"https://doi.org/10.1109/icdmw69685.2025.00039","title":"An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling","display_name":"An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling","publication_year":2025,"publication_date":"2025-11-12","ids":{"openalex":"https://openalex.org/W4415251289","doi":"https://doi.org/10.1109/icdmw69685.2025.00039"},"language":"en","primary_location":{"id":"doi:10.1109/icdmw69685.2025.00039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2502.17873","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100528194","display_name":"Jiazhen Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210117529","display_name":"Pivot Bio (United States)","ror":"https://ror.org/01wm6dg61","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiazhen Hong","raw_affiliation_strings":["Emotiv Research,Melbourne,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emotiv Research,Melbourne,Australia","institution_ids":["https://openalex.org/I4210117529"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080426650","display_name":"Geoffrey Mackellar","orcid":"https://orcid.org/0000-0001-5657-6226"},"institutions":[{"id":"https://openalex.org/I4210117529","display_name":"Pivot Bio (United States)","ror":"https://ror.org/01wm6dg61","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Geoffrey Mackellar","raw_affiliation_strings":["Emotiv Research,Sydney,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emotiv Research,Sydney,Australia","institution_ids":["https://openalex.org/I4210117529"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058158283","display_name":"Soheila Ghane","orcid":"https://orcid.org/0000-0001-7906-6456"},"institutions":[{"id":"https://openalex.org/I4210117529","display_name":"Pivot Bio (United States)","ror":"https://ror.org/01wm6dg61","country_code":"US","type":"company","lineage":["https://openalex.org/I4210117529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Soheila Ghane","raw_affiliation_strings":["Emotiv Research,Melbourne,Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Emotiv Research,Melbourne,Australia","institution_ids":["https://openalex.org/I4210117529"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210117529"],"apc_list":null,"apc_paid":null,"fwci":1.5371,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.84737736,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"285","last_page":"292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9495000243186951,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9495000243186951,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/generalization","display_name":"Generalization","score":0.7376999855041504},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5525000095367432},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5501000285148621},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.536300003528595},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.5},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.44589999318122864},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4156999886035919},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.37619999051094055},{"id":"https://openalex.org/keywords/transferability","display_name":"Transferability","score":0.37209999561309814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8023999929428101},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7376999855041504},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5985000133514404},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5525000095367432},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5501000285148621},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.536300003528595},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.5},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.44589999318122864},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3855000138282776},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.37619999051094055},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.37209999561309814},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32749998569488525},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2797999978065491},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.2734000086784363},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2540999948978424}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icdmw69685.2025.00039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdmw69685.2025.00039","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Data Mining Workshops (ICDMW)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2502.17873","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.17873","pdf_url":"https://arxiv.org/pdf/2502.17873","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2502.17873","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2502.17873","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2502.17873","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2502.17873","pdf_url":"https://arxiv.org/pdf/2502.17873","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415251289.pdf","grobid_xml":"https://content.openalex.org/works/W4415251289.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2027927824","https://openalex.org/W4316660615","https://openalex.org/W4295514134","https://openalex.org/W4401863902","https://openalex.org/W3177342940","https://openalex.org/W4415797794","https://openalex.org/W1901129140","https://openalex.org/W2916853398","https://openalex.org/W2543240607","https://openalex.org/W2292901520","https://openalex.org/W4385219276","https://openalex.org/W2894692871","https://openalex.org/W2963587345","https://openalex.org/W3213412168","https://openalex.org/W4294975317"],"related_works":[],"abstract_inverted_index":{"Electroencephalogram":[0],"(EEG)":[1],"signals":[2],"generally":[3],"exhibit":[4],"low":[5],"signal-to-noise":[6],"ratio":[7],"(SNR)":[8],"and":[9,17,46,109,127,158,169,183,186,202,215],"high":[10],"inter-subject":[11],"variability,":[12],"making":[13,49],"generalization":[14,163,199],"across":[15,200,208],"subjects":[16,201],"domains":[18],"challenging.":[19],"Recent":[20],"advances":[21],"in":[22,34,131,180],"deep":[23],"learning,":[24],"particularly":[25],"self-supervised":[26,85,147],"learning":[27],"with":[28,98],"Transformer-based":[29],"architectures,":[30],"have":[31],"shown":[32],"promise":[33],"EEG":[35,133,153],"representation":[36],"learning.":[37],"However,":[38],"their":[39],"quadratic":[40],"computational":[41,103],"complexity":[42],"increases":[43],"memory":[44,107],"usage":[45,108],"slows":[47],"inference,":[48],"them":[50],"inefficient":[51],"for":[52,219],"modeling":[53,185],"long-range":[54,129],"dependencies.":[55],"Moreover,":[56,143],"most":[57],"existing":[58,195],"approaches":[59],"emphasize":[60],"either":[61],"explicit":[62],"window":[63],"segmentation":[64],"of":[65,119],"the":[66,114,122],"temporal":[67,76,167],"signal":[68],"or":[69,138],"spectral-only":[70],"input":[71],"embedding":[72],"while":[73],"neglecting":[74],"raw":[75,132,152],"dynamics.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81],"propose":[82],"EEGM2,":[83],"a":[84,93,146,155],"framework":[86],"that":[87,150,175,191],"overcomes":[88],"these":[89],"limitations.":[90],"EEGM2":[91,144,176,192,211],"adopts":[92],"U-shaped":[94],"encoder-decoder":[95],"architecture":[96],"integrated":[97],"Mamba-2":[99,120],"to":[100,124],"achieve":[101],"linear":[102],"complexity,":[104],"thereby":[105],"reducing":[106],"improving":[110],"inference":[111],"speed.":[112],"Meanwhile,":[113],"selective":[115],"information":[116],"propagation":[117],"mechanism":[118],"enables":[121],"model":[123],"effectively":[125],"capture":[126],"preserve":[128],"dependencies":[130],"signals,":[134],"where":[135],"traditional":[136],"RNN":[137],"CNN":[139],"architectures":[140],"often":[141],"struggle.":[142],"employs":[145],"pre-training":[148],"objective":[149],"reconstructs":[151],"using":[154],"combined":[156],"L1":[157],"spectral":[159,170],"(Fourier-based)":[160],"loss,":[161],"enhancing":[162],"by":[164],"jointly":[165],"preserving":[166],"dynamics":[168],"characteristics.":[171],"Experimental":[172],"results":[173],"demonstrate":[174],"achieves":[177],"state-of-the-art":[178],"performance":[179],"both":[181],"short-":[182],"long-sequence":[184],"classification.":[187],"Further":[188],"evaluations":[189],"show":[190],"consistently":[193],"outperforms":[194],"models,":[196],"demonstrating":[197],"strong":[198],"tasks,":[203],"as":[204,206],"well":[205],"transferability":[207],"domains.":[209],"Overall,":[210],"offers":[212],"an":[213],"efficient":[214],"scalable":[216],"solution":[217],"suitable":[218],"deployment":[220],"on":[221],"resource-constrained":[222],"brain-computer":[223],"interface":[224],"(BCI)":[225],"devices.":[226]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-09T07:27:16.801131","created_date":"2025-10-16T00:00:00"}