{"id":"https://openalex.org/W4409152871","doi":"https://doi.org/10.26599/bdma.2024.9020071","title":"Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment","display_name":"Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment","publication_year":2025,"publication_date":"2025-04-04","ids":{"openalex":"https://openalex.org/W4409152871","doi":"https://doi.org/10.26599/bdma.2024.9020071"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2024.9020071","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020071","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.26599/bdma.2024.9020071","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103217215","display_name":"Xing Jin","orcid":"https://orcid.org/0000-0001-6927-581X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xing Jin","raw_affiliation_strings":["College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085581846","display_name":"Fa Zhu","orcid":"https://orcid.org/0000-0002-7089-5386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fa Zhu","raw_affiliation_strings":["College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014176442","display_name":"Yu Shen","orcid":"https://orcid.org/0000-0002-3899-7868"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu Shen","raw_affiliation_strings":["College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology & Artificial Intelligence,Nanjing,China,210037","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049449184","display_name":"Gwanggil Jeon","orcid":"https://orcid.org/0000-0002-0651-4278"},"institutions":[{"id":"https://openalex.org/I146429904","display_name":"Incheon National University","ror":"https://ror.org/02xf7p935","country_code":"KR","type":"education","lineage":["https://openalex.org/I146429904"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gwanggil Jeon","raw_affiliation_strings":["Incheon National University,Department of Embedded Systems Engineering,Incheon,Republic of Korea,22012"],"affiliations":[{"raw_affiliation_string":"Incheon National University,Department of Embedded Systems Engineering,Incheon,Republic of Korea,22012","institution_ids":["https://openalex.org/I146429904"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025314362","display_name":"David Camacho","orcid":"https://orcid.org/0000-0002-5051-3475"},"institutions":[{"id":"https://openalex.org/I88060688","display_name":"Universidad Polit\u00e9cnica de Madrid","ror":"https://ror.org/03n6nwv02","country_code":"ES","type":"education","lineage":["https://openalex.org/I88060688"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"David Camacho","raw_affiliation_strings":["Universidad Politécnica de Madrid,Computer Systems Engineering Department,Madrid,Spain,28660"],"affiliations":[{"raw_affiliation_string":"Universidad Politécnica de Madrid,Computer Systems Engineering Department,Madrid,Spain,28660","institution_ids":["https://openalex.org/I88060688"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103217215"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.9376,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.98229232,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"8","issue":"3","first_page":"712","last_page":"725"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8978999853134155,"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":0.8978999853134155,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8321999907493591,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.8259999752044678,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6616462469100952},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4785771369934082},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4619349241256714},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4550947844982147},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4312148094177246},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4136626720428467},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35964280366897583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3520375192165375},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23503568768501282},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.178024560213089},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.12704864144325256},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1098705530166626},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10664933919906616},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10406902432441711},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.09207436442375183}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6616462469100952},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4785771369934082},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4619349241256714},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4550947844982147},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4312148094177246},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4136626720428467},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35964280366897583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3520375192165375},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23503568768501282},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.178024560213089},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.12704864144325256},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1098705530166626},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10664933919906616},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10406902432441711},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.09207436442375183}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.26599/bdma.2024.9020071","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020071","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:defb9ea321e44225a39410be067ea9d9","is_oa":true,"landing_page_url":"https://doaj.org/article/defb9ea321e44225a39410be067ea9d9","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 8, Iss 3, Pp 712-725 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2024.9020071","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2024.9020071","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1947251450","https://openalex.org/W1970727126","https://openalex.org/W2002055708","https://openalex.org/W2038610431","https://openalex.org/W2043181832","https://openalex.org/W2064770205","https://openalex.org/W2289714406","https://openalex.org/W2572280252","https://openalex.org/W2746986823","https://openalex.org/W2790404832","https://openalex.org/W2799160222","https://openalex.org/W2890694965","https://openalex.org/W2903959724","https://openalex.org/W2950982700","https://openalex.org/W2962905870","https://openalex.org/W2993348073","https://openalex.org/W3024961463","https://openalex.org/W3033046106","https://openalex.org/W3035101080","https://openalex.org/W3045665366","https://openalex.org/W3047405387","https://openalex.org/W3083299806","https://openalex.org/W3088256290","https://openalex.org/W3109093849","https://openalex.org/W3154396742","https://openalex.org/W3177392932","https://openalex.org/W3199715002","https://openalex.org/W3211938211","https://openalex.org/W4200042752","https://openalex.org/W4205558134","https://openalex.org/W4213177906","https://openalex.org/W4226080951","https://openalex.org/W4280583458","https://openalex.org/W4281763922","https://openalex.org/W4291910522","https://openalex.org/W4294308527","https://openalex.org/W4312597583","https://openalex.org/W4316171088","https://openalex.org/W4316655758","https://openalex.org/W4318815957","https://openalex.org/W4318816006","https://openalex.org/W4323927812","https://openalex.org/W4361994982","https://openalex.org/W4365448982","https://openalex.org/W4372260286","https://openalex.org/W4379141350","https://openalex.org/W4381167182","https://openalex.org/W4386262510","https://openalex.org/W4386365702","https://openalex.org/W4386973857","https://openalex.org/W4390205023","https://openalex.org/W4391661564","https://openalex.org/W4392769733","https://openalex.org/W4396751917","https://openalex.org/W4399727982","https://openalex.org/W4399984843","https://openalex.org/W4399989589","https://openalex.org/W4400645501","https://openalex.org/W4405598593","https://openalex.org/W6676605431"],"related_works":["https://openalex.org/W2922348724","https://openalex.org/W200322357","https://openalex.org/W2130428257","https://openalex.org/W4308951944","https://openalex.org/W2057366091","https://openalex.org/W4312960290","https://openalex.org/W2049513647","https://openalex.org/W2988848585","https://openalex.org/W4233722919","https://openalex.org/W4323929055"],"abstract_inverted_index":{"With":[0],"the":[1,22,25,34,46,77,81,86,122,129,137,145,154,170,176,181,187,194,204,212,237,253],"rapid":[2],"progress":[3],"in":[4,21,33,108,121,175],"data-driven":[5,53,99],"approaches,":[6],"artificial":[7],"intelligence,":[8],"and":[9,54,66,135,147,157,240,245,249],"big":[10,55],"data":[11,56,113],"analytics":[12,57],"technologies,":[13],"utilizing":[14],"electroencephalogram":[15],"(EEG)":[16],"signals":[17,120],"for":[18,52,115,179,247],"emotion":[19,41,116],"analysis":[20,42,117,223],"field":[23],"of":[24,27,36,48,80,111,118,124,184,214,234,242],"Internet":[26],"Medical":[28],"Things":[29],"can":[30,191],"effectively":[31,152,192],"assist":[32],"diagnosis":[35],"specific":[37],"diseases.":[38],"While":[39],"existing":[40],"methods":[43],"focus":[44],"on":[45,236,252],"utilization":[47],"effective":[49],"deep":[50],"models":[51],"technology,":[58],"they":[59],"often":[60],"struggle":[61],"to":[62,143,151,210],"extract":[63,128],"long-range":[64,155],"dependencies":[65],"accurately":[67],"model":[68],"local":[69,158],"relationships":[70],"within":[71],"multi-channel":[72],"EEG":[73,119,221],"signals.":[74],"In":[75],"addition,":[76],"subjective":[78],"scores":[79],"subjects":[82],"may":[83],"not":[84],"match":[85],"predefined":[87],"emotional":[88],"labels.":[89],"To":[90],"overcome":[91],"these":[92],"limitations,":[93],"this":[94],"paper":[95],"proposes":[96],"a":[97,161,226],"new":[98],"dynamic":[100,139,171],"graph-embedded":[101],"Transformer":[102,177],"network":[103],"(DGETN)":[104],"that":[105],"has":[106,230],"emerged":[107],"different":[109,198],"tasks":[110],"graph":[112,172,188],"mining":[114,180],"scene":[123],"IoMT.":[125],"Firstly,":[126],"we":[127,202],"frequency":[130],"features":[131,183],"differential":[132],"entropy":[133],"(DE)":[134],"use":[136],"linear":[138],"system":[140],"(LDS)":[141],"method":[142],"alleviate":[144,211],"redundancy":[146],"noise":[148],"information.":[149],"Secondly,":[150],"explore":[153],"information":[156,196],"modeling":[159],"ability,":[160],"novel":[162],"feature":[163],"extraction":[164],"module":[165],"is":[166],"designed":[167],"by":[168],"embedding":[169],"convolution":[173,189],"operations":[174,190],"encoder":[178],"discriminant":[182],"data.":[185],"Moreover,":[186],"exploit":[193],"spatial":[195],"between":[197],"channels.":[199],"At":[200],"last,":[201],"introduce":[203],"minimum":[205],"category":[206],"confusion":[207],"(MCC)":[208],"loss":[209],"fuzziness":[213],"classification.":[215],"We":[216],"take":[217],"two":[218],"commonly":[219],"used":[220],"sentiment":[222],"datasets":[224],"as":[225],"study.":[227],"The":[228],"DGETN":[229],"achieved":[231],"state-of-the-art":[232],"accuracies":[233,241],"99.38%":[235],"SEED":[238],"dataset,":[239,255],"99.24":[243],"%":[244],"98.85%":[246],"valence":[248],"arousal":[250],"prediction":[251],"DEAP":[254],"respectively.":[256]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5}],"updated_date":"2026-04-01T17:29:45.350535","created_date":"2025-10-10T00:00:00"}