{"id":"https://openalex.org/W7127419361","doi":"https://doi.org/10.1109/rivf68649.2025.11365173","title":"Deep Learning-Based Emotion Recognition Using Electroencephalogram Signals","display_name":"Deep Learning-Based Emotion Recognition Using Electroencephalogram Signals","publication_year":2025,"publication_date":"2025-12-18","ids":{"openalex":"https://openalex.org/W7127419361","doi":"https://doi.org/10.1109/rivf68649.2025.11365173"},"language":null,"primary_location":{"id":"doi:10.1109/rivf68649.2025.11365173","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rivf68649.2025.11365173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 RIVF International Conference on Computing and Communication Technologies (RIVF)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100981040","display_name":"Duy Tien Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I165735259","display_name":"Gunma University","ror":"https://ror.org/046fm7598","country_code":"JP","type":"education","lineage":["https://openalex.org/I165735259"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Duy Nguyen","raw_affiliation_strings":["Graduate School of Science and Technology, Gunma University,Gunma,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Gunma University,Gunma,Japan","institution_ids":["https://openalex.org/I165735259"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Anh Nguyen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210150999","display_name":"Research Institute of Posts and Telecommunications","ror":"https://ror.org/04j808v60","country_code":"SK","type":"nonprofit","lineage":["https://openalex.org/I4210150999"]}],"countries":["SK"],"is_corresponding":false,"raw_author_name":"Anh Nguyen","raw_affiliation_strings":["Institute of Technology,Faculty of of Artificial Intelligence Posts and Telecommunications,Hanoi,Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Technology,Faculty of of Artificial Intelligence Posts and Telecommunications,Hanoi,Vietnam","institution_ids":["https://openalex.org/I4210150999"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014854859","display_name":"Minh Tu\u1ea5n Nguy\u1ec5n","orcid":"https://orcid.org/0000-0002-7034-5544"},"institutions":[{"id":"https://openalex.org/I4210095603","display_name":"Vietnam Posts and Telecommunications Group (Vietnam)","ror":"https://ror.org/00q0e7f94","country_code":"VN","type":"company","lineage":["https://openalex.org/I4210095603"]},{"id":"https://openalex.org/I4210150999","display_name":"Research Institute of Posts and Telecommunications","ror":"https://ror.org/04j808v60","country_code":"SK","type":"nonprofit","lineage":["https://openalex.org/I4210150999"]}],"countries":["SK","VN"],"is_corresponding":false,"raw_author_name":"Minh Tuan Nguyen","raw_affiliation_strings":["Institute of Technology,Faculty of Telecommunications Posts and Telecommunications,Hanoi,Vietnam"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Technology,Faculty of Telecommunications Posts and Telecommunications,Hanoi,Vietnam","institution_ids":["https://openalex.org/I4210095603","https://openalex.org/I4210150999"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114556436","display_name":"Kou Yamada","orcid":null},"institutions":[{"id":"https://openalex.org/I165735259","display_name":"Gunma University","ror":"https://ror.org/046fm7598","country_code":"JP","type":"education","lineage":["https://openalex.org/I165735259"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kou Yamada","raw_affiliation_strings":["Graduate School of Science and Technology, Gunma University,Gunma,Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Science and Technology, Gunma University,Gunma,Japan","institution_ids":["https://openalex.org/I165735259"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67424483,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"197","last_page":"202"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8865000009536743,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.8865000009536743,"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":0.07930000126361847,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.002099999925121665,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5649999976158142},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.531499981880188},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.531000018119812},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5267999768257141},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49470001459121704},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4352000057697296},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.4336000084877014},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4259999990463257},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.4156000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7325000166893005},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6184999942779541},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5649999976158142},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.531499981880188},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5267999768257141},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4878999888896942},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4352000057697296},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.38100001215934753},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.3199999928474426},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27320000529289246},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25690001249313354}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rivf68649.2025.11365173","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rivf68649.2025.11365173","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 RIVF International Conference on Computing and Communication Technologies (RIVF)","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":19,"referenced_works":["https://openalex.org/W2002055708","https://openalex.org/W2134031328","https://openalex.org/W2255466643","https://openalex.org/W3043108545","https://openalex.org/W3108087271","https://openalex.org/W3168997536","https://openalex.org/W3208033968","https://openalex.org/W3217034758","https://openalex.org/W4205740271","https://openalex.org/W4229024336","https://openalex.org/W4250640407","https://openalex.org/W4311900644","https://openalex.org/W4319738477","https://openalex.org/W4386932779","https://openalex.org/W4387053553","https://openalex.org/W4392905733","https://openalex.org/W4402526082","https://openalex.org/W4404431930","https://openalex.org/W4408975519"],"related_works":[],"abstract_inverted_index":{"Emotion":[0],"recognition":[1],"plays":[2],"an":[3,41,112,126,138,152],"important":[4],"role":[5],"in":[6,25],"various":[7],"aspects":[8],"of":[9,33,65,114,118,122,128,140,144,148,156],"daily":[10],"life,":[11],"including":[12],"education,":[13],"healthcare,":[14],"and":[15,23,92,125,151,172],"entertainment.":[16],"Moreover,":[17],"intelligent":[18],"emotion":[19,48,176],"prediction":[20],"enhances":[21],"adaptability":[22],"responsiveness":[24],"brain-computer":[26],"interface":[27],"systems,":[28],"thereby":[29],"improving":[30],"the":[31,63,103,108,166],"efficiency":[32],"human-computer":[34],"interaction.":[35],"In":[36],"this":[37,135],"paper,":[38],"we":[39],"propose":[40],"efficient":[42],"hybrid":[43,94],"deep":[44],"learning":[45],"model":[46,97,106,136],"for":[47,131,159,175,183],"detection":[49],"using":[50,98],"electroencephalogram":[51],"(EEG)":[52],"signals.":[53],"The":[54,74],"framework":[55,168],"first":[56],"performs":[57],"optimal":[58],"channel":[59],"selection,":[60],"followed":[61],"by":[62],"extraction":[64],"band":[66],"power":[67],"features":[68],"from":[69],"four":[70],"EEG":[71],"frequency":[72],"bands.":[73],"resulting":[75],"feature":[76],"sets":[77],"are":[78],"then":[79],"used":[80],"to":[81],"validate":[82],"One-Dimension":[83],"Convolutional":[84],"Neural":[85],"Network":[86],"(1D-CNN),":[87],"Long-Short":[88],"Term":[89],"Memories":[90],"(LSTM),":[91],"a":[93,170,180],"1D":[95,104],"CNN-LSTM":[96,105],"5-fold":[99],"cross-validation.":[100],"Among":[101],"them,":[102],"achieves":[107,137],"best":[109],"performance":[110],"with":[111],"accuracy":[113,139],"95.00":[115],"%,":[116,120,124,142,146,150],"precision":[117,143],"95.93":[119],"recall":[121,147],"95.07":[123],"F1-score":[127],"95.50":[129],"%":[130,158],"arousal":[132],"classification.":[133,161],"Similarly,":[134],"94.85":[141],"95.12":[145],"95.02":[149],"F":[153],"1":[154],"-score":[155],"94.95":[157],"valence":[160],"These":[162],"results":[163],"demonstrate":[164],"that":[165],"proposed":[167],"offers":[169],"robust":[171],"generalizable":[173],"solution":[174],"recognition,":[177],"making":[178],"it":[179],"promising":[181],"approach":[182],"practical":[184],"applications.":[185]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-02-04T00:00:00"}
