{"id":"https://openalex.org/W7083018407","doi":"https://doi.org/10.1109/access.2025.3613355","title":"Eye-Blink and SSVEP-Based Selective Attention Interface for XR User Authentication: An Explainable Neural Decoding and Machine Learning Approach to Reducing Visual Fatigue","display_name":"Eye-Blink and SSVEP-Based Selective Attention Interface for XR User Authentication: An Explainable Neural Decoding and Machine Learning Approach to Reducing Visual Fatigue","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083018407","doi":"https://doi.org/10.1109/access.2025.3613355"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3613355","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613355","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3613355","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Min Seong Chae","orcid":"https://orcid.org/0009-0008-5198-8246"},"institutions":[{"id":"https://openalex.org/I70591777","display_name":"Jeonju University","ror":"https://ror.org/015v9d997","country_code":"KR","type":"education","lineage":["https://openalex.org/I70591777"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Min Seong Chae","raw_affiliation_strings":["Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0008-5198-8246","affiliations":[{"raw_affiliation_string":"Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea","institution_ids":["https://openalex.org/I70591777"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Abdul Rehman","orcid":"https://orcid.org/0000-0002-9343-7652"},"institutions":[{"id":"https://openalex.org/I70591777","display_name":"Jeonju University","ror":"https://ror.org/015v9d997","country_code":"KR","type":"education","lineage":["https://openalex.org/I70591777"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Abdul Rehman","raw_affiliation_strings":["Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-9343-7652","affiliations":[{"raw_affiliation_string":"Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea","institution_ids":["https://openalex.org/I70591777"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yeni Kim","orcid":"https://orcid.org/0009-0002-9249-4636"},"institutions":[{"id":"https://openalex.org/I70591777","display_name":"Jeonju University","ror":"https://ror.org/015v9d997","country_code":"KR","type":"education","lineage":["https://openalex.org/I70591777"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yeni Kim","raw_affiliation_strings":["Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0002-9249-4636","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea","institution_ids":["https://openalex.org/I70591777"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jaedeok Kim","orcid":"https://orcid.org/0009-0008-1952-7482"},"institutions":[{"id":"https://openalex.org/I70591777","display_name":"Jeonju University","ror":"https://ror.org/015v9d997","country_code":"KR","type":"education","lineage":["https://openalex.org/I70591777"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jaedeok Kim","raw_affiliation_strings":["Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0008-1952-7482","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering, Jeonju University, Jeonju, Republic of Korea","institution_ids":["https://openalex.org/I70591777"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yaeeun Han","orcid":"https://orcid.org/0009-0002-6868-6443"},"institutions":[{"id":"https://openalex.org/I58716616","display_name":"Korea Institute of Science and Technology","ror":"https://ror.org/05kzfa883","country_code":"KR","type":"facility","lineage":["https://openalex.org/I27494661","https://openalex.org/I2801339556","https://openalex.org/I2801339556","https://openalex.org/I4210144908","https://openalex.org/I4387152098","https://openalex.org/I4387152098","https://openalex.org/I58716616"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yaeeun Han","raw_affiliation_strings":["Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0002-6868-6443","affiliations":[{"raw_affiliation_string":"Bionics Research Center, Korea Institute of Science and Technology, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I58716616"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sangin Park","orcid":"https://orcid.org/0000-0003-2992-5122"},"institutions":[{"id":"https://openalex.org/I4210106485","display_name":"GeneMatrix (South Korea)","ror":"https://ror.org/012b6r490","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210106485"]},{"id":"https://openalex.org/I4210121370","display_name":"Next Generation Technology (United States)","ror":"https://ror.org/02hswyz47","country_code":"US","type":"company","lineage":["https://openalex.org/I4210121370"]}],"countries":["KR","US"],"is_corresponding":false,"raw_author_name":"Sangin Park","raw_affiliation_strings":["Next-Generation Mechanical Design Laboratory, Korea University, Seoul, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-2992-5122","affiliations":[{"raw_affiliation_string":"Next-Generation Mechanical Design Laboratory, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I4210106485","https://openalex.org/I4210121370"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sungchul Mun","orcid":"https://orcid.org/0000-0003-4596-9889"},"institutions":[{"id":"https://openalex.org/I70591777","display_name":"Jeonju University","ror":"https://ror.org/015v9d997","country_code":"KR","type":"education","lineage":["https://openalex.org/I70591777"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungchul Mun","raw_affiliation_strings":["Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0003-4596-9889","affiliations":[{"raw_affiliation_string":"Convergence Institute of Human Data Technology, Jeonju University, Jeonju, Republic of Korea","institution_ids":["https://openalex.org/I70591777"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.396,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.88721234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":null,"first_page":"176998","last_page":"177018"},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.383899986743927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.383899986743927,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13177","display_name":"Geological and Geophysical Studies","score":0.04820000007748604,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.027300000190734863,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6399999856948853},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5403000116348267},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.48350000381469727},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47620001435279846},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.46650001406669617},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4641000032424927},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.46160000562667847},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.45329999923706055},{"id":"https://openalex.org/keywords/authentication","display_name":"Authentication (law)","score":0.43810001015663147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8342999815940857},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6399999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5861999988555908},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5013999938964844},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.48350000381469727},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47620001435279846},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.46650001406669617},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C148417208","wikidata":"https://www.wikidata.org/wiki/Q4825882","display_name":"Authentication (law)","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3921000063419342},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3774999976158142},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3643999993801117},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.35249999165534973},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.34700000286102295},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2694000005722046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2678000032901764},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.26489999890327454},{"id":"https://openalex.org/C89505385","wikidata":"https://www.wikidata.org/wiki/Q47146","display_name":"User interface","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.25949999690055847},{"id":"https://openalex.org/C2779338814","wikidata":"https://www.wikidata.org/wiki/Q5179285","display_name":"Covert","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3613355","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613355","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3d14c67f87c7438a82c838bb4ec65f30","is_oa":true,"landing_page_url":"https://doaj.org/article/3d14c67f87c7438a82c838bb4ec65f30","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 176998-177018 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3613355","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3613355","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.707305908203125,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G7639089420","display_name":null,"funder_award_id":"RS-2023-00247958","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G892340930","display_name":null,"funder_award_id":"RS-2024-00340293","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,223],"growing":[1],"demand":[2],"for":[3,13,68,195,231],"secure,":[4],"immersive":[5,69],"authentication":[6,31,217],"in":[7,233],"extended":[8],"reality":[9],"(XR)":[10],"environments":[11],"calls":[12],"neural":[14,50],"interfaces":[15],"that":[16,33,209],"are":[17,54],"both":[18,196],"robust":[19,28],"and":[20,27,42,101,118,149,166,171,191,203,229,237],"user-friendly.":[21],"This":[22],"study":[23],"introduces":[24],"a":[25,57,87,95],"novel":[26],"dual-modality":[29],"EEG-based":[30],"framework":[32,186],"independently":[34,104],"exploits":[35],"(i)":[36],"Steady-State":[37],"Visually":[38],"Evoked":[39],"Potentials":[40],"(SSVEP)":[41],"(ii)":[43],"voluntary":[44],"eye-blink\u2013induced":[45],"EEG":[46,59,212],"responses":[47],"as":[48],"covert":[49],"signatures.":[51],"Both":[52],"signals":[53,103],"recorded":[55],"using":[56,156,182],"64-channel":[58],"system":[60,225],"seamlessly":[61],"integrated":[62],"with":[63,86],"the":[64,130,175],"Microsoft":[65],"HoloLens":[66],"2":[67],"XR-based":[70,234],"user":[71],"evaluation.":[72],"To":[73],"mitigate":[74],"visual":[75,91],"fatigue":[76],"while":[77],"preserving":[78],"signal":[79],"fidelity,":[80],"we":[81],"replace":[82],"conventional":[83],"flicker":[84],"stimuli":[85],"10":[88,132],"Hz":[89],"grow\u2013shrink":[90],"design.":[92],"We":[93],"employ":[94],"modality-specific":[96],"classification":[97],"strategy,":[98],"modeling":[99],"SSVEP":[100],"eye-blink":[102],"to":[105,128,145],"retain":[106],"their":[107],"distinct":[108],"neurophysiological":[109],"characteristics.":[110],"A":[111],"multi-stage":[112],"feature":[113],"selection":[114],"pipeline":[115],"combines":[116],"SHAP":[117],"Random":[119,169],"Forest":[120,170],"rankings,":[121],"followed":[122],"by":[123],"logistic":[124],"regression\u2013based":[125],"permutation":[126],"importance":[127],"identify":[129],"top":[131],"discriminative":[133],"features":[134,138,213],"per":[135],"modality.":[136],"These":[137],"undergo":[139],"statistical":[140],"validation":[141],"via":[142],"non-parametric":[143],"tests":[144],"ensure":[146],"physiological":[147],"plausibility":[148],"class":[150],"separability.":[151],"Classification":[152],"is":[153,226],"subsequently":[154],"performed":[155],"four":[157],"machine":[158],"learning":[159],"models\u2014Random":[160],"Forest,":[161],"XGBoost,":[162],"Support":[163],"Vector":[164],"Machine,":[165],"Logistic":[167],"Regression\u2014with":[168],"XGBoost":[172],"consistently":[173],"yielding":[174],"highest":[176],"performance.":[177],"Evaluated":[178],"across":[179],"20":[180],"participants":[181],"user-wise":[183],"validation,":[184],"our":[185],"achieves":[187],"over":[188],"99%":[189],"accuracy":[190],"near-perfect":[192],"ROC-AUC":[193],"scores":[194],"modalities,":[197],"confirming":[198],"strong":[199],"discriminability":[200],"between":[201],"genuine":[202],"impostor":[204],"attempts.":[205],"Our":[206],"results":[207],"demonstrate":[208],"interpretable,":[210],"fatigue-aware":[211],"can":[214],"deliver":[215],"high":[216],"performance":[218],"under":[219],"real-time":[220],"XR":[221],"conditions.":[222],"proposed":[224],"lightweight,":[227],"spoof-resistant,":[228],"well-suited":[230],"deployment":[232],"defense,":[235],"training,":[236],"industrial":[238],"applications.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}