{"id":"https://openalex.org/W7133335560","doi":"https://doi.org/10.1109/ijcb65343.2025.11411476","title":"EEG-based User Authentication in Realistic Scenarios: From Solo Reading to Dialog Games","display_name":"EEG-based User Authentication in Realistic Scenarios: From Solo Reading to Dialog Games","publication_year":2025,"publication_date":"2025-09-08","ids":{"openalex":"https://openalex.org/W7133335560","doi":"https://doi.org/10.1109/ijcb65343.2025.11411476"},"language":null,"primary_location":{"id":"doi:10.1109/ijcb65343.2025.11411476","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","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/A5028317321","display_name":"Chi Xu","orcid":"https://orcid.org/0000-0001-5480-3974"},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Chi Xu","raw_affiliation_strings":["The University of Osaka,D3 Center"],"affiliations":[{"raw_affiliation_string":"The University of Osaka,D3 Center","institution_ids":["https://openalex.org/I14314212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128004027","display_name":"Xiang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["The University of Osaka,D3 Center"],"affiliations":[{"raw_affiliation_string":"The University of Osaka,D3 Center","institution_ids":["https://openalex.org/I14314212"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002148629","display_name":"Shuqiong Wu","orcid":"https://orcid.org/0000-0003-1501-9719"},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shuqiong Wu","raw_affiliation_strings":["The University of Osaka,D3 Center"],"affiliations":[{"raw_affiliation_string":"The University of Osaka,D3 Center","institution_ids":["https://openalex.org/I14314212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040773064","display_name":"Yasushi Yagi","orcid":"https://orcid.org/0000-0002-3546-8071"},"institutions":[{"id":"https://openalex.org/I14314212","display_name":"Osaka University of Economics","ror":"https://ror.org/04g11bp59","country_code":"JP","type":"education","lineage":["https://openalex.org/I14314212"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasushi Yagi","raw_affiliation_strings":["The University of Osaka,D3 Center"],"affiliations":[{"raw_affiliation_string":"The University of Osaka,D3 Center","institution_ids":["https://openalex.org/I14314212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5028317321"],"corresponding_institution_ids":["https://openalex.org/I14314212"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88843037,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11800","display_name":"User Authentication and Security Systems","score":0.44130000472068787,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11800","display_name":"User Authentication and Security Systems","score":0.44130000472068787,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.2556999921798706,"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":0.0689999982714653,"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/softmax-function","display_name":"Softmax function","score":0.5982000231742859},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.5473999977111816},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5386999845504761},{"id":"https://openalex.org/keywords/biometrics","display_name":"Biometrics","score":0.5044999718666077},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.45509999990463257},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4251999855041504},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.4244999885559082},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4180999994277954},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3928999900817871}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8073999881744385},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.5982000231742859},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.5473999977111816},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5386999845504761},{"id":"https://openalex.org/C184297639","wikidata":"https://www.wikidata.org/wiki/Q177765","display_name":"Biometrics","level":2,"score":0.5044999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4862000048160553},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4855000078678131},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.45509999990463257},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4180999994277954},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.375},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3677999973297119},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3440999984741211},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3402000069618225},{"id":"https://openalex.org/C173853756","wikidata":"https://www.wikidata.org/wiki/Q86915","display_name":"Dialog box","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C2993756790","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading aloud","level":3,"score":0.3109000027179718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C194699767","wikidata":"https://www.wikidata.org/wiki/Q7878662","display_name":"Multi-factor authentication","level":4,"score":0.2720000147819519},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C161615301","wikidata":"https://www.wikidata.org/wiki/Q309396","display_name":"Keystroke logging","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcb65343.2025.11411476","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcb65343.2025.11411476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Joint Conference on Biometrics (IJCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7332049608230591}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W97381929","https://openalex.org/W1543094396","https://openalex.org/W1547702425","https://openalex.org/W1955857676","https://openalex.org/W1975277950","https://openalex.org/W1977020076","https://openalex.org/W2060472216","https://openalex.org/W2063483288","https://openalex.org/W2067530554","https://openalex.org/W2097245267","https://openalex.org/W2134577338","https://openalex.org/W2210717610","https://openalex.org/W2288961935","https://openalex.org/W2293554103","https://openalex.org/W2302255633","https://openalex.org/W2527798123","https://openalex.org/W2585659631","https://openalex.org/W2612085556","https://openalex.org/W2736583283","https://openalex.org/W2911312349","https://openalex.org/W2940209306","https://openalex.org/W2945325110","https://openalex.org/W2969985801","https://openalex.org/W2980272688","https://openalex.org/W3030443594","https://openalex.org/W4200550087","https://openalex.org/W4236173595","https://openalex.org/W4251666178","https://openalex.org/W4282981111","https://openalex.org/W4323338328","https://openalex.org/W4386148071","https://openalex.org/W4392853578","https://openalex.org/W4394698527","https://openalex.org/W4398141882","https://openalex.org/W4405284552"],"related_works":[],"abstract_inverted_index":{"While":[0],"electroencephalography":[1],"(EEG)-based":[2],"user":[3,27,85],"authentication":[4,28,86,216],"has":[5],"demonstrated":[6],"strong":[7],"potential":[8],"in":[9,15,29,67,139],"controlled":[10,207],"laboratory":[11],"conditions,":[12,208],"its":[13],"reliability":[14],"more":[16,228],"realistic":[17,30],"settings":[18],"remains":[19],"underexplored.":[20],"In":[21],"this":[22],"work,":[23],"we":[24,91,110,188],"investigate":[25],"EEG-based":[26,80,225],"interactive":[31],"scenarios":[32,56],"involving":[33],"cognitively":[34],"and":[35,51,65,74,99,105,123,170,205],"behaviorally":[36],"rich":[37],"tasks.":[38],"We":[39],"collected":[40],"EEG":[41,115,152,209],"data":[42],"from":[43],"subjects":[44],"under":[45,88,102,203],"two":[46],"conditions:":[47],"solo":[48],"reading":[49],"aloud":[50],"two-person":[52],"dialog-based":[53],"games.":[54],"These":[55,154,218],"represent":[57],"practical,":[58],"everyday":[59],"activities":[60],"where":[61],"users":[62],"produce":[63],"speech":[64],"engage":[66],"turn-taking":[68],"interactions,":[69],"introducing":[70],"non-stationarity,":[71],"muscle":[72],"artifacts,":[73],"attention":[75],"shifts":[76],"that":[77,146,201],"challenge":[78],"conventional":[79],"models.":[81],"To":[82,183],"evaluate":[83],"the":[84,113,148,190,221],"performance":[87],"these":[89],"challenges,":[90],"systematically":[92],"apply":[93],"various":[94,160],"deep":[95],"learning":[96],"backbone":[97,161],"models":[98],"loss":[100,192,195],"functions":[101],"a":[103,128,134,140],"fair":[104],"consistent":[106],"experimental":[107],"protocol.":[108],"Specifically,":[109],"first":[111],"divide":[112],"whole":[114],"signal":[116],"into":[117,159],"short":[118,166],"windows":[119],"with":[120],"pre-defined":[121],"length":[122],"stride.":[124],"For":[125],"each":[126],"window,":[127],"time-frequency":[129,144],"representation":[130],"is":[131],"computed":[132],"using":[133],"continuous":[135],"wavelet":[136],"transform,":[137],"resulting":[138],"sequence":[141],"of":[142,151,223],"2D":[143],"maps":[145,155],"capture":[147],"non-stationary":[149],"characteristics":[150],"signals.":[153],"are":[156,176],"then":[157],"fed":[158],"models,":[162],"such":[163],"as":[164],"long":[165],"term":[167],"memory":[168],"(LSTM)":[169],"convolutional":[171],"neural":[172],"network":[173],"(CNN),":[174],"which":[175],"trained":[177],"to":[178,227],"extract":[179],"robust":[180],"identity-discriminative":[181],"features.":[182],"further":[184],"enhance":[185],"inter-subject":[186],"separability,":[187],"employ":[189],"Softmax":[191],"or":[193],"ArcFace":[194],"during":[196],"training.":[197],"Experimental":[198],"results":[199],"demonstrate":[200],"even":[202],"dynamic":[204],"less":[206],"signals":[210],"retain":[211],"individual-specific":[212],"patterns,":[213],"yielding":[214],"high":[215],"accuracy.":[217],"findings":[219],"highlight":[220],"feasibility":[222],"extending":[224],"biometrics":[226],"natural":[229],"environments.":[230]},"counts_by_year":[],"updated_date":"2026-03-05T07:30:30.508283","created_date":"2026-03-04T00:00:00"}