{"id":"https://openalex.org/W7161954821","doi":"https://doi.org/10.48550/arxiv.2605.21280","title":"Let EEG Models Learn EEG","display_name":"Let EEG Models Learn EEG","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161954821","doi":"https://doi.org/10.48550/arxiv.2605.21280"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21280","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.21280","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136644600","display_name":"Wang, Yifan, 1963-","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136661066","display_name":"Yijia Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Yijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136635499","display_name":"Wen Li","orcid":"https://orcid.org/0000-0003-3495-4550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076320750","display_name":"Chenyu You","orcid":"https://orcid.org/0000-0001-8365-7822"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"You, Chenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9172000288963318,"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":0.9172000288963318,"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/T10581","display_name":"Neural dynamics and brain function","score":0.014600000344216824,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.008999999612569809,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7200000286102295},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4196999967098236},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41839998960494995},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3824999928474426},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3792000114917755},{"id":"https://openalex.org/keywords/discretization","display_name":"Discretization","score":0.336899995803833},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3206999897956848}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7200000286102295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6620000004768372},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5613999962806702},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3824999928474426},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3792000114917755},{"id":"https://openalex.org/C73000952","wikidata":"https://www.wikidata.org/wiki/Q17007827","display_name":"Discretization","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3264999985694885},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31299999356269836},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2676999866962433},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26249998807907104},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21280","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":"doi:10.48550/arxiv.2605.21280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21280","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.4796484410762787,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"High-fidelity":[0],"EEG":[1,24,76,93,106,125,156,208],"generation":[2,25,77],"is":[3],"critical":[4],"for":[5],"alleviating":[6],"data":[7,126],"scarcity":[8],"and":[9,38,56,62,132,168,204],"addressing":[10],"privacy":[11],"constraints":[12,161],"in":[13,59],"large-scale":[14,173],"neural":[15,42,88,199],"modeling.":[16],"Despite":[17],"recent":[18],"progress,":[19],"most":[20],"existing":[21],"approaches":[22],"formulate":[23],"via":[26],"discrete":[27],"denoising":[28,139],"objectives,":[29],"which":[30],"inadequately":[31],"reflect":[32],"the":[33,60,66,84,124,147],"inherently":[34],"continuous":[35,85,112],"temporal":[36,54,63,130,166],"dynamics":[37,134,149],"spectral":[39,61,164],"structure":[40,64],"of":[41,65,87,155,198],"activity.":[43],"As":[44],"a":[45,96,116,202],"result,":[46],"these":[47],"methods":[48],"often":[49],"struggle":[50],"to":[51,123,186,207],"preserve":[52,163],"long-range":[53],"dependencies":[55],"exhibit":[57],"mismatches":[58],"generated":[67],"signals.":[68,89],"In":[69],"this":[70],"work,":[71],"we":[72,158],"argue":[73],"that":[74,80,104,120,146,162,192],"effective":[75],"requires":[78],"models":[79,105],"operate":[81],"directly":[82],"on":[83,100,137],"evolution":[86],"We":[90],"introduce":[91,159],"Just":[92],"Transformer":[94],"(JET),":[95],"generative":[97],"framework":[98],"based":[99],"conditional":[101],"flow":[102],"matching":[103],"as":[107],"raw":[108],"sequences":[109],"evolving":[110],"along":[111],"trajectories.":[113],"By":[114],"learning":[115],"smooth":[117],"vector":[118],"field":[119],"transports":[121],"noise":[122],"distribution,":[127],"JET":[128,175,193],"captures":[129,194],"continuity":[131],"transient":[133],"without":[135],"relying":[136],"discretized":[138],"schemes":[140],"or":[141],"domain-specific":[142],"representations.":[143],"To":[144],"ensure":[145],"learned":[148],"remain":[150],"consistent":[151],"with":[152],"key":[153,195],"properties":[154,197],"signals,":[157],"principled":[160,205],"structure,":[165],"stationarity,":[167],"signal-level":[169],"statistics.":[170],"Across":[171],"three":[172],"benchmarks,":[174],"consistently":[176],"achieves":[177],"state-of-the-art":[178],"performance,":[179],"reducing":[180],"TS-FID":[181],"by":[182],"over":[183],"40%":[184],"compared":[185],"strong":[187],"baselines.":[188],"Extensive":[189],"analyses":[190],"show":[191],"structural":[196],"dynamics,":[200],"providing":[201],"scalable":[203],"approach":[206],"generation.":[209],"Project":[210],"page:":[211],"https://y-research-sbu.github.io/JET/":[212],".":[213]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-22T00:00:00"}
