{"id":"https://openalex.org/W7126068995","doi":"https://doi.org/10.1109/bibm66473.2025.11356582","title":"On the Use of Graph Convolutional Network for Detecting Major Depressive Disorders using EEG Signals","display_name":"On the Use of Graph Convolutional Network for Detecting Major Depressive Disorders using EEG Signals","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126068995","doi":"https://doi.org/10.1109/bibm66473.2025.11356582"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356582","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/A5124172137","display_name":"Zakaria Alaimia","orcid":null},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I48825208","display_name":"Universit\u00e9 Paris 8","ror":"https://ror.org/04wez5e68","country_code":"FR","type":"education","lineage":["https://openalex.org/I48825208"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Zakaria Alaimia","raw_affiliation_strings":["University of Paris 8,LIASD Laboratory,France"],"affiliations":[{"raw_affiliation_string":"University of Paris 8,LIASD Laboratory,France","institution_ids":["https://openalex.org/I48825208","https://openalex.org/I204730241"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124082950","display_name":"Larbi Boubchir","orcid":null},"institutions":[{"id":"https://openalex.org/I204730241","display_name":"Universit\u00e9 Paris Cit\u00e9","ror":"https://ror.org/05f82e368","country_code":"FR","type":"education","lineage":["https://openalex.org/I204730241"]},{"id":"https://openalex.org/I48825208","display_name":"Universit\u00e9 Paris 8","ror":"https://ror.org/04wez5e68","country_code":"FR","type":"education","lineage":["https://openalex.org/I48825208"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Larbi Boubchir","raw_affiliation_strings":["University of Paris 8,LIASD Laboratory,France"],"affiliations":[{"raw_affiliation_string":"University of Paris 8,LIASD Laboratory,France","institution_ids":["https://openalex.org/I48825208","https://openalex.org/I204730241"]}]},{"author_position":"last","author":{"id":null,"display_name":"Youssef Elmir","orcid":null},"institutions":[{"id":"https://openalex.org/I142476485","display_name":"\u00c9cole Polytechnique","ror":"https://ror.org/05hy3tk52","country_code":"FR","type":"education","lineage":["https://openalex.org/I142476485","https://openalex.org/I4210145102"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Youssef Elmir","raw_affiliation_strings":["Ecole sup&#x00E9;rieure en Sciences et Technologies de l&#x0027;Informatique et du Num&#x00E9;rique,Laboratoire LITAN,Bejaia,Alg&#x00E9;rie"],"affiliations":[{"raw_affiliation_string":"Ecole sup&#x00E9;rieure en Sciences et Technologies de l&#x0027;Informatique et du Num&#x00E9;rique,Laboratoire LITAN,Bejaia,Alg&#x00E9;rie","institution_ids":["https://openalex.org/I142476485"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5124172137"],"corresponding_institution_ids":["https://openalex.org/I204730241","https://openalex.org/I48825208"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.72928169,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6944","last_page":"6949"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.7386000156402588,"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":0.7386000156402588,"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":0.1501999944448471,"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"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.0272000003606081,"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/generalizability-theory","display_name":"Generalizability theory","score":0.8072999715805054},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6498000025749207},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4925999939441681},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4700999855995178},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.43540000915527344},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4007999897003174},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3774000108242035}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8072999715805054},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7038000226020813},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6498000025749207},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5920000076293945},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5356000065803528},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4925999939441681},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4700999855995178},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3028999865055084},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356582","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356582","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":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1566775803","https://openalex.org/W1936982107","https://openalex.org/W2019885188","https://openalex.org/W2040975718","https://openalex.org/W2054294508","https://openalex.org/W2056555744","https://openalex.org/W2105957367","https://openalex.org/W2106822551","https://openalex.org/W2134050473","https://openalex.org/W2519531315","https://openalex.org/W2526511911","https://openalex.org/W2800428573","https://openalex.org/W2907492528","https://openalex.org/W4231449374","https://openalex.org/W4234326366","https://openalex.org/W4284887989"],"related_works":[],"abstract_inverted_index":{"Major":[0],"Depressive":[1],"Disorder":[2],"(MDD)":[3],"is":[4],"a":[5,39,81,95,122,158],"significant":[6],"public":[7],"health":[8],"concern":[9],"for":[10,164],"which":[11],"diagnosis":[12],"remains":[13],"subjective":[14],"and":[15,67,79,93,114,131,161],"challenging.":[16],"While":[17],"conventional":[18],"deep":[19],"learning":[20],"models":[21],"show":[22],"limited":[23],"success":[24],"on":[25],"EEG":[26],"data,":[27],"the":[28,48,104,107,135,146,152],"Graph":[29],"Input":[30],"layer":[31],"attention":[32],"Convolutional":[33],"Network":[34],"(GICN)":[35],"has":[36],"recently":[37],"set":[38],"new":[40],"state-of-the-art":[41],"benchmark":[42,163],"with":[43],"~":[44],"96%":[45],"accuracy.":[46],"However,":[47],"reliability":[49],"of":[50,106,125,138],"such":[51,60],"high-performance":[52],"benchmarks":[53],"can":[54,72],"be":[55],"compromised":[56],"by":[57],"methodological":[58],"flaws,":[59],"as":[61],"subject-level":[62],"data":[63],"leakage":[64],"between":[65],"training":[66],"testing":[68],"sets.":[69],"This":[70,149],"issue":[71],"lead":[73],"to":[74,85,102,111],"overly":[75],"optimistic":[76],"performance":[77,105,155],"estimates":[78],"hinders":[80],"model's":[82,153],"true":[83],"generalizability":[84],"unseen":[86],"patients.":[87],"To":[88],"address":[89],"this,":[90],"we":[91],"proposed":[92],"implemented":[94],"rigorous":[96],"10-fold":[97],"subject-wise":[98],"cross-validation":[99],"framework":[100],"allowing":[101],"improve":[103],"GICN":[108,147],"model":[109,120],"also":[110],"ensure":[112],"robust":[113],"generalizable":[115],"evaluation.":[116],"Our":[117],"methodologically":[118],"sound":[119],"achieved":[121],"mean":[123],"accuracy":[124],"97.05%":[126],"<tex":[127,140],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[128,141],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\pm":[129,142],"1.44\\%$</tex>":[130],"an":[132],"Area":[133],"Under":[134],"Curve":[136],"(AUC)":[137],"0.9918":[139],"0.0033$</tex>,":[143],"outperforming":[144],"thun":[145],"model.":[148],"result":[150],"confirms":[151],"high":[154],"while":[156],"establishing":[157],"more":[159],"reliable":[160],"stable":[162],"EEG-based":[165],"MDD":[166],"detection.":[167]},"counts_by_year":[],"updated_date":"2026-03-25T23:56:10.502304","created_date":"2026-01-30T00:00:00"}
