{"id":"https://openalex.org/W7126114934","doi":"https://doi.org/10.1109/bibm66473.2025.11356595","title":"GCDE: Graph-Embedded Conditional Diffusion for EEG Data Augmentation","display_name":"GCDE: Graph-Embedded Conditional Diffusion for EEG Data Augmentation","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126114934","doi":"https://doi.org/10.1109/bibm66473.2025.11356595"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356595","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 Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100743062","display_name":"Xuhui Wang","orcid":"https://orcid.org/0000-0001-5819-5718"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuhui Wang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430876","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0003-3415-5345"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshan Zhang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056463157","display_name":"Kui Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kui Zhao","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033698712","display_name":"Xicheng Zhang","orcid":"https://orcid.org/0000-0003-3721-1357"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu Zhang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100743062"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.71433167,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4636","last_page":"4643"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9837999939918518,"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":0.9837999939918518,"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":0.003000000026077032,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7174000144004822},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.652899980545044},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4544000029563904},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.4251999855041504},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.39500001072883606},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.3700000047683716},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3384000062942505},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.3294999897480011}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245000004768372},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7174000144004822},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.652899980545044},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.621999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4763000011444092},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.4251999855041504},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3294999897480011},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C75564084","wikidata":"https://www.wikidata.org/wiki/Q5597085","display_name":"Graph embedding","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356595","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356595","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 Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2255870710","display_name":null,"funder_award_id":"62376219,62006194","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1947251450","https://openalex.org/W2027782798","https://openalex.org/W2030727443","https://openalex.org/W2119163516","https://openalex.org/W2132360759","https://openalex.org/W2140413964","https://openalex.org/W2142197234","https://openalex.org/W2165177179","https://openalex.org/W2559463885","https://openalex.org/W2741907166","https://openalex.org/W2949736877","https://openalex.org/W2992520770","https://openalex.org/W2996290406","https://openalex.org/W3048643622","https://openalex.org/W3080551539","https://openalex.org/W3096831136","https://openalex.org/W4230962939","https://openalex.org/W4312933868","https://openalex.org/W4327521769","https://openalex.org/W4385245566","https://openalex.org/W4386047604","https://openalex.org/W4399890154","https://openalex.org/W4401946594","https://openalex.org/W4406259310"],"related_works":[],"abstract_inverted_index":{"The":[0],"inherent":[1,119],"scarcity":[2],"of":[3,12,71,155,173],"high-quality":[4,227],"electroencephalography":[5],"(EEG)":[6],"datasets":[7],"critically":[8],"constrains":[9],"the":[10,29,54,69,103,113,136,144,163,167,174,186],"development":[11],"robust":[13],"brain-computer":[14],"interface":[15],"(BCI).":[16],"Data":[17],"augmentation":[18,33,50,232],"has":[19],"thus":[20],"emerged":[21],"as":[22],"a":[23,46,98,220],"crucial":[24],"strategy":[25],"for":[26,59,225],"artificially":[27],"enlarging":[28],"dataset.":[30],"However,":[31],"existing":[32],"frameworks":[34],"often":[35],"struggle":[36],"to":[37,64,89,101,127,200],"generate":[38,90],"highfidelity":[39],"signals.":[40,92,121],"In":[41],"this":[42],"paper,":[43],"we":[44,123,181],"propose":[45],"novel":[47],"EEG":[48,62,72,109,120,228],"data":[49,169,231],"framework":[51,224],"based":[52],"on":[53,135,185],"Graph-Embedded":[55],"Conditional":[56],"Diffusion":[57],"model":[58,165],"generating":[60,226],"artificial":[61],"(GCDE)":[63],"augment":[65],"dataset":[66,141],"and":[67,78,116,142,160,190,206,222,235],"improve":[68],"performance":[70,149],"decoder.":[73],"Unlike":[74],"Variational":[75],"Autoencoders":[76],"(VAE)":[77],"Generative":[79],"Adversarial":[80],"Networks":[81],"(GAN),":[82],"GCDE":[83,134,151,218],"employs":[84],"an":[85],"iterative":[86],"denoising":[87],"process":[88],"realistic":[91],"We":[93,132],"integrate":[94],"Graph":[95],"Embedding":[96],"within":[97],"U-Net":[99],"architecture":[100],"learn":[102],"spatial":[104],"topological":[105],"relationships":[106],"among":[107],"multiple":[108],"electrodes,":[110],"thereby":[111],"capturing":[112],"complex":[114],"temporal":[115],"neuroscientific":[117],"significance":[118],"Additionally,":[122],"incorporate":[124],"label":[125],"embedding":[126],"enable":[128],"conditional,":[129],"classspecific":[130],"generation.":[131],"evaluate":[133],"BCI":[137,234],"Competition":[138],"IV":[139],"2a":[140],"investigate":[143],"optimal":[145],"ratio":[146],"that":[147,217],"maximizes":[148],"improvements.":[150],"achieves":[152],"significant":[153],"improvements":[154],"6.95":[156],"%":[157,172],"in":[158,162,233],"EEGNet":[159,198],"3.36%":[161],"ST-GF":[164],"when":[166],"generated":[168],"constitutes":[170],"75":[171],"real":[175],"data.":[176],"To":[177],"validate":[178],"its":[179],"generalizability,":[180],"further":[182],"conduct":[183],"experiments":[184],"SEED":[187],"(emotion":[188],"recognition)":[189],"Fatigue":[191],"(fatigue":[192],"detection)":[193],"datasets,":[194],"where":[195],"accuracy":[196],"with":[197],"increase":[199],"<tex":[201,207],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[202,208],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$98.87":[203],"\\%(+2.64":[204],"\\%)$</tex>":[205],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$93.81":[209],"\\%(+3.60":[210],"\\%)$</tex>,":[211],"respectively.":[212],"These":[213],"comprehensive":[214],"results":[215],"demonstrate":[216],"is":[219],"powerful":[221],"generalizable":[223],"signals,":[229],"advancing":[230],"other":[236],"EEGbased":[237],"domains.":[238],"Our":[239],"code":[240],"will":[241],"be":[242],"released":[243],"upon":[244],"acceptance.":[245]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-30T00:00:00"}