{"id":"https://openalex.org/W4295206973","doi":"https://doi.org/10.3390/s22186813","title":"Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition","display_name":"Accelerating 3D Convolutional Neural Network with Channel Bottleneck Module for EEG-Based Emotion Recognition","publication_year":2022,"publication_date":"2022-09-08","ids":{"openalex":"https://openalex.org/W4295206973","doi":"https://doi.org/10.3390/s22186813","pmid":"https://pubmed.ncbi.nlm.nih.gov/36146160"},"language":"en","primary_location":{"id":"doi:10.3390/s22186813","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22186813","pdf_url":"https://www.mdpi.com/1424-8220/22/18/6813/pdf?version=1662694286","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/18/6813/pdf?version=1662694286","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047974322","display_name":"Sungkyu Kim","orcid":"https://orcid.org/0000-0002-9216-0882"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungkyu Kim","raw_affiliation_strings":["Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"],"raw_orcid":"https://orcid.org/0000-0002-9216-0882","affiliations":[{"raw_affiliation_string":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102935360","display_name":"Tae\u2010Seong Kim","orcid":"https://orcid.org/0009-0008-2751-3267"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Tae-Seong Kim","raw_affiliation_strings":["Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering, Kyung Hee University, Yongin 17104, Korea","institution_ids":["https://openalex.org/I35928602"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114549787","display_name":"Won Hee Lee","orcid":"https://orcid.org/0009-0007-9308-6550"},"institutions":[{"id":"https://openalex.org/I35928602","display_name":"Kyung Hee University","ror":"https://ror.org/01zqcg218","country_code":"KR","type":"education","lineage":["https://openalex.org/I35928602"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Won Hee Lee","raw_affiliation_strings":["Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea","institution_ids":["https://openalex.org/I35928602"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114549787"],"corresponding_institution_ids":["https://openalex.org/I35928602"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":1.8929,"has_fulltext":true,"cited_by_count":17,"citation_normalized_percentile":{"value":0.8553014,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"22","issue":"18","first_page":"6813","last_page":"6813"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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":1.0,"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":0.9998000264167786,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8276023864746094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7607731819152832},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7377034425735474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6886084675788879},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6653597950935364},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5847411155700684},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5643364787101746},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.43741580843925476},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.41276082396507263},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06518295407295227}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8276023864746094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7607731819152832},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7377034425735474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6886084675788879},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6653597950935364},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5847411155700684},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5643364787101746},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.43741580843925476},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.41276082396507263},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06518295407295227},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D001143","descriptor_name":"Arousal","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001143","descriptor_name":"Arousal","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001143","descriptor_name":"Arousal","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":5,"locations":[{"id":"doi:10.3390/s22186813","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22186813","pdf_url":"https://www.mdpi.com/1424-8220/22/18/6813/pdf?version=1662694286","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36146160","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36146160","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:0ade09c172de4c85bb0fb5e540b0e3b7","is_oa":true,"landing_page_url":"https://doaj.org/article/0ade09c172de4c85bb0fb5e540b0e3b7","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":"Sensors, Vol 22, Iss 18, p 6813 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/18/6813/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22186813","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 18; Pages: 6813","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9500982","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9500982","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22186813","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22186813","pdf_url":"https://www.mdpi.com/1424-8220/22/18/6813/pdf?version=1662694286","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1197061048","display_name":null,"funder_award_id":"2017-0-00655","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G8650542281","display_name":null,"funder_award_id":"2021R1C1C1009436","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4295206973.pdf","grobid_xml":"https://content.openalex.org/works/W4295206973.grobid-xml"},"referenced_works_count":52,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1965902103","https://openalex.org/W1966647632","https://openalex.org/W2002055708","https://openalex.org/W2011123320","https://openalex.org/W2022795097","https://openalex.org/W2035518691","https://openalex.org/W2036124792","https://openalex.org/W2064675550","https://openalex.org/W2068759427","https://openalex.org/W2074716497","https://openalex.org/W2081420711","https://openalex.org/W2082776552","https://openalex.org/W2083242503","https://openalex.org/W2097117768","https://openalex.org/W2100495367","https://openalex.org/W2132083787","https://openalex.org/W2148394752","https://openalex.org/W2149628368","https://openalex.org/W2194775991","https://openalex.org/W2368869802","https://openalex.org/W2395611524","https://openalex.org/W2396728763","https://openalex.org/W2398040377","https://openalex.org/W2565944610","https://openalex.org/W2584561145","https://openalex.org/W2625929003","https://openalex.org/W2765856398","https://openalex.org/W2772766867","https://openalex.org/W2782273434","https://openalex.org/W2944401411","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2981372722","https://openalex.org/W3004735003","https://openalex.org/W3032135501","https://openalex.org/W3044186523","https://openalex.org/W3084230554","https://openalex.org/W3093125198","https://openalex.org/W3108087271","https://openalex.org/W3119911037","https://openalex.org/W3140416091","https://openalex.org/W3189300284","https://openalex.org/W3203998598","https://openalex.org/W3206241967","https://openalex.org/W3206744364","https://openalex.org/W3217075389","https://openalex.org/W4200175924","https://openalex.org/W4200322992","https://openalex.org/W6745586887"],"related_works":["https://openalex.org/W2595172197","https://openalex.org/W2084856301","https://openalex.org/W2127970246","https://openalex.org/W2885125400","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"emotion":[2,17,45,78,211],"recognition":[3,18,46,212],"using":[4],"EEG":[5,105,119,189],"has":[6,205],"received":[7],"increasing":[8],"attention":[9],"in":[10,22,92,129,139],"recent":[11],"years.":[12],"The":[13,180],"existing":[14],"studies":[15],"on":[16,163,187],"show":[19],"great":[20],"variability":[21],"their":[23],"employed":[24],"methods":[25],"including":[26],"the":[27,34,54,81,85,108,124,133,136,140,164,170,193],"choice":[28],"of":[29,36,56,83,104,110,135,154,172],"deep":[30,40],"learning":[31,41],"approaches":[32],"and":[33,126,142,156,160,176],"type":[35],"input":[37,109],"features.":[38],"Although":[39],"models":[42],"for":[43,76,158,208],"EEG-based":[44,77],"can":[47],"deliver":[48],"superior":[49],"accuracy,":[50],"it":[51],"comes":[52],"at":[53],"cost":[55],"high":[57],"computational":[58],"complexity.":[59],"Here,":[60],"we":[61,98],"propose":[62],"a":[63,70,89,100,201],"novel":[64],"3D":[65,101,188],"convolutional":[66],"neural":[67],"network":[68],"with":[69,80,183,200],"channel":[71],"bottleneck":[72],"module":[73],"(CNN-BN)":[74],"model":[75,116,138,149,182,199],"recognition,":[79],"aim":[82],"accelerating":[84,209],"CNN":[86],"computation":[87],"without":[88,213],"significant":[90],"loss":[91],"classification":[93,144,215],"accuracy.":[94],"To":[95],"this":[96],"end,":[97],"constructed":[99],"spatiotemporal":[102,118,190],"representation":[103,191],"signals":[106],"as":[107],"our":[111],"proposed":[112,147,197],"model.":[113],"Our":[114,146,196],"CNN-BN":[115,137,148,181,198],"extracts":[117],"features,":[120],"which":[121],"effectively":[122],"utilize":[123],"spatial":[125],"temporal":[127],"information":[128],"EEG.":[130],"We":[131],"evaluated":[132],"performance":[134],"valence":[141,159],"arousal":[143],"tasks.":[145],"achieved":[150],"an":[151],"average":[152],"accuracy":[153],"99.1%":[155],"99.5%":[157],"arousal,":[161],"respectively,":[162],"DEAP":[165],"dataset,":[166],"while":[167],"significantly":[168],"reducing":[169],"number":[171],"parameters":[173,185],"by":[174,178],"93.08%":[175],"FLOPs":[177],"94.94%.":[179],"fewer":[184],"based":[186],"outperforms":[192],"state-of-the-art":[194],"models.":[195],"better":[202],"parameter":[203],"efficiency":[204],"excellent":[206],"potential":[207],"CNN-based":[210],"losing":[214],"performance.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
