{"id":"https://openalex.org/W4409640601","doi":"https://doi.org/10.1109/tits.2025.3559098","title":"Multimodal Driver Drowsiness Detection Using Facial Expressions and Ear-EEGs With a Lightweight Auto-Denoising Network","display_name":"Multimodal Driver Drowsiness Detection Using Facial Expressions and Ear-EEGs With a Lightweight Auto-Denoising Network","publication_year":2025,"publication_date":"2025-04-21","ids":{"openalex":"https://openalex.org/W4409640601","doi":"https://doi.org/10.1109/tits.2025.3559098"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2025.3559098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3559098","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5003335735","display_name":"Ngoc-Dau Mai","orcid":"https://orcid.org/0009-0005-7027-1505"},"institutions":[{"id":"https://openalex.org/I8991828","display_name":"Pukyong National University","ror":"https://ror.org/0433kqc49","country_code":"KR","type":"education","lineage":["https://openalex.org/I8991828"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Ngoc-Dau Mai","raw_affiliation_strings":["Department of AI Convergence, Pukyong National University, Busan, Republic of Korea"],"raw_orcid":"https://orcid.org/0009-0005-7027-1505","affiliations":[{"raw_affiliation_string":"Department of AI Convergence, Pukyong National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I8991828"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033760891","display_name":"Ha-Trung Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I8991828","display_name":"Pukyong National University","ror":"https://ror.org/0433kqc49","country_code":"KR","type":"education","lineage":["https://openalex.org/I8991828"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ha-Trung Nguyen","raw_affiliation_strings":["Department of AI Convergence, Pukyong National University, Busan, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of AI Convergence, Pukyong National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I8991828"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079773768","display_name":"Wan\u2010Young Chung","orcid":"https://orcid.org/0000-0002-0121-855X"},"institutions":[{"id":"https://openalex.org/I8991828","display_name":"Pukyong National University","ror":"https://ror.org/0433kqc49","country_code":"KR","type":"education","lineage":["https://openalex.org/I8991828"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wan-Young Chung","raw_affiliation_strings":["Department of AI Convergence, Pukyong National University, Busan, Republic of Korea"],"raw_orcid":"https://orcid.org/0000-0002-0121-855X","affiliations":[{"raw_affiliation_string":"Department of AI Convergence, Pukyong National University, Busan, Republic of Korea","institution_ids":["https://openalex.org/I8991828"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003335735"],"corresponding_institution_ids":["https://openalex.org/I8991828"],"apc_list":null,"apc_paid":null,"fwci":6.5727,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.96177582,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"26","issue":"6","first_page":"7819","last_page":"7832"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9940000176429749,"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"}},"topics":[{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.9940000176429749,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9539999961853027,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.902999997138977,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.6410184502601624},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6324730515480042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5939492583274841},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5150842070579529},{"id":"https://openalex.org/keywords/facial-expression","display_name":"Facial expression","score":0.4746090769767761},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.47410139441490173},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4563993513584137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41763442754745483},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.15294551849365234},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11845916509628296}],"concepts":[{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6410184502601624},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6324730515480042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5939492583274841},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5150842070579529},{"id":"https://openalex.org/C195704467","wikidata":"https://www.wikidata.org/wiki/Q327968","display_name":"Facial expression","level":2,"score":0.4746090769767761},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.47410139441490173},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4563993513584137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41763442754745483},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.15294551849365234},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11845916509628296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3559098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3559098","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"},{"score":0.46000000834465027,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W131819148","https://openalex.org/W1890200061","https://openalex.org/W1970070014","https://openalex.org/W2055264244","https://openalex.org/W2066210406","https://openalex.org/W2067530554","https://openalex.org/W2068763596","https://openalex.org/W2085497225","https://openalex.org/W2105271344","https://openalex.org/W2116646930","https://openalex.org/W2138964882","https://openalex.org/W2150665176","https://openalex.org/W2194775991","https://openalex.org/W2236853541","https://openalex.org/W2242976112","https://openalex.org/W2254832494","https://openalex.org/W2341528187","https://openalex.org/W2513317220","https://openalex.org/W2558580397","https://openalex.org/W2605163429","https://openalex.org/W2611482981","https://openalex.org/W2892075859","https://openalex.org/W2896966314","https://openalex.org/W2908578648","https://openalex.org/W2912729179","https://openalex.org/W2914237854","https://openalex.org/W2922251765","https://openalex.org/W2950922080","https://openalex.org/W2963446712","https://openalex.org/W2979646541","https://openalex.org/W2982083293","https://openalex.org/W2992048516","https://openalex.org/W3043319350","https://openalex.org/W3080551539","https://openalex.org/W3114396675","https://openalex.org/W3133565772","https://openalex.org/W4247596867","https://openalex.org/W4294975390","https://openalex.org/W4381988482","https://openalex.org/W6798893201"],"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":{"Integrating":[0],"computer":[1,41,121],"vision":[2],"and":[3,46,60,82,88,120,131,155,185,229,240],"physiological":[4],"analysis":[5,45,119],"in":[6,129,140,152,167,207],"driver":[7,208],"drowsiness":[8,137,209],"detection":[9,133,210],"(DDD)":[10],"is":[11,198],"a":[12,30,35,56,86,112],"promising":[13],"technology":[14],"for":[15,63],"accurately":[16],"identifying":[17],"drowsy":[18],"states":[19],"while":[20,138],"driving,":[21],"thereby":[22],"preventing":[23],"potentially":[24],"dangerous":[25],"accidents.":[26],"This":[27],"study":[28,143],"proposes":[29],"multimodal":[31,113,193],"DDD":[32,65,114,245],"system":[33,66],"with":[34,179,195,203,215],"deep":[36],"neural":[37],"network":[38,115,174],"that":[39],"combines":[40],"vision-based":[42,122],"face":[43,123],"expression":[44,124],"electroencephalogram":[47],"(EEG)":[48],"data":[49],"analysis.":[50],"Key":[51],"contributions":[52],"include:":[53],"1)":[54],"providing":[55],"comprehensive":[57],"hardware,":[58],"firmware,":[59],"software":[61],"design":[62],"the":[64,104,107,135,145,161,164,238],"to":[67,79,93,126,158,200],"acquire":[68],"behind-the-ear":[69],"(BTE)":[70],"EEG":[71,97,118],"signals,":[72],"rather":[73],"than":[74],"conventional":[75],"scalp":[76],"EEGs,":[77],"due":[78],"their":[80],"convenience":[81],"practicality;":[83],"2)":[84],"proposing":[85],"powerful":[87],"lightweight":[89],"GAN-based":[90,172,196],"auto-denoising":[91,173,197],"method":[92],"eliminate":[94],"artifacts":[95],"from":[96],"signals":[98],"during":[99],"signal":[100],"acquisition,":[101],"significantly":[102],"influencing":[103],"quality":[105],"of":[106,134,163,183,188,218,221,224,227,232,242],"obtained":[108],"result;":[109],"3)":[110],"developing":[111],"by":[116],"combining":[117],"identification":[125],"improve":[127],"performance":[128,162],"monitoring":[130],"early":[132],"driver\u2019s":[136],"engaging":[139],"traffic.":[141],"The":[142,170,190,234],"employs":[144],"relative":[146],"root":[147],"mean":[148],"squared":[149],"error":[150],"(RRMSE)":[151],"both":[153],"temporal":[154],"spectral":[156],"domains":[157],"quantitatively":[159],"assess":[160],"proposed":[165,171,191,244],"approaches":[166,206],"artifact":[168],"removal.":[169],"outperforms":[175],"other":[176,201],"comparable":[177],"approaches,":[178],"an":[180,216,230],"RRMSE":[181,186],"(temporal)":[182],"0.210":[184],"(spectral)":[187],"0.161.":[189],"trained":[192],"model":[194],"superior":[199],"models":[202],"different":[204],"denoising":[205],"across":[211],"all":[212],"five-evaluation":[213],"metrics,":[214],"accuracy":[217],"95.33%,":[219],"specificity":[220],"95.48%,":[222],"sensitivity":[223],"95.17%,":[225],"precision":[226],"95.47%,":[228],"F1-score":[231],"95.32%.":[233],"experimental":[235],"results":[236],"demonstrate":[237],"practicality":[239],"feasibility":[241],"our":[243],"system.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-05-21T09:19:25.381259","created_date":"2025-10-10T00:00:00"}