{"id":"https://openalex.org/W4313525692","doi":"https://doi.org/10.1109/bibm55620.2022.9994941","title":"CR-GAT: Consistency Regularization Enhanced Graph Attention Network for Semi-supervised EEG Emotion Recognition","display_name":"CR-GAT: Consistency Regularization Enhanced Graph Attention Network for Semi-supervised EEG Emotion Recognition","publication_year":2022,"publication_date":"2022-12-06","ids":{"openalex":"https://openalex.org/W4313525692","doi":"https://doi.org/10.1109/bibm55620.2022.9994941"},"language":"en","primary_location":{"id":"doi:10.1109/bibm55620.2022.9994941","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9994941","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5074318258","display_name":"Jiyao Liu","orcid":"https://orcid.org/0000-0002-3316-5704"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiyao Liu","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031229559","display_name":"Hao Wu","orcid":"https://orcid.org/0000-0003-3063-2263"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Wu","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100425719","display_name":"Li Zhang","orcid":"https://orcid.org/0000-0003-1641-7831"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Zhang","raw_affiliation_strings":["Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University,School of Computer Science,Xi&#x2019;an,Shaanxi,China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"2017","last_page":"2023"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9993000030517578,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.9958999752998352,"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/discriminative-model","display_name":"Discriminative model","score":0.7357423305511475},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6783032417297363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.659437894821167},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6534178256988525},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6086547374725342},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6059818863868713},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5068958401679993},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.48011159896850586},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.441464900970459},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.35433030128479004},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.10844376683235168},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.10482332110404968}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7357423305511475},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6783032417297363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659437894821167},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6534178256988525},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6086547374725342},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6059818863868713},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5068958401679993},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.48011159896850586},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.441464900970459},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35433030128479004},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.10844376683235168},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.10482332110404968},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm55620.2022.9994941","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/bibm55620.2022.9994941","pdf_url":null,"source":{"id":"https://openalex.org/S4363607730","display_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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":43,"referenced_works":["https://openalex.org/W205159212","https://openalex.org/W1596717185","https://openalex.org/W1947251450","https://openalex.org/W1970727126","https://openalex.org/W2002055708","https://openalex.org/W2040232951","https://openalex.org/W2115403315","https://openalex.org/W2132889650","https://openalex.org/W2156503193","https://openalex.org/W2158726522","https://openalex.org/W2187089797","https://openalex.org/W2291663305","https://openalex.org/W2559463885","https://openalex.org/W2584561145","https://openalex.org/W2594083602","https://openalex.org/W2599124244","https://openalex.org/W2765856398","https://openalex.org/W2786768213","https://openalex.org/W2790404832","https://openalex.org/W2889782437","https://openalex.org/W2911632638","https://openalex.org/W2934123712","https://openalex.org/W2941401350","https://openalex.org/W2964321699","https://openalex.org/W2970007912","https://openalex.org/W2996553713","https://openalex.org/W3024961463","https://openalex.org/W3032135501","https://openalex.org/W3091002423","https://openalex.org/W3102455230","https://openalex.org/W3108087271","https://openalex.org/W3121810080","https://openalex.org/W3128719470","https://openalex.org/W3158278463","https://openalex.org/W3159301005","https://openalex.org/W3205821217","https://openalex.org/W4223980685","https://openalex.org/W4225411558","https://openalex.org/W4250442959","https://openalex.org/W6608394925","https://openalex.org/W6696405891","https://openalex.org/W6720006811","https://openalex.org/W6802697691"],"related_works":["https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W1482209366","https://openalex.org/W2110523656","https://openalex.org/W2521627374","https://openalex.org/W2032664813"],"abstract_inverted_index":{"Electroencephalogram":[0],"(EEG)":[1],"emotion":[2,123],"recognition":[3],"has":[4],"become":[5],"a":[6,74,82,110,184],"research":[7],"focus":[8],"in":[9,51],"the":[10,17,29,39,105,133,140,151,170,175,189,207,214,217,228,237,252,256],"field":[11],"of":[12,19,25,31,41,48,73,77,89,129,188,227,260],"human-computer":[13],"interaction":[14],"(HCI).":[15],"However,":[16],"process":[18],"EEG":[20,33,79,122,166,176,190],"signal":[21],"collection":[22],"requires":[23],"lots":[24],"expertise,":[26],"which":[27,44,57,196],"makes":[28],"amount":[30,76],"labeled":[32,67,97,201],"data":[34,50],"very":[35],"limited.":[36],"It":[37],"constrains":[38],"performance":[40],"supervised":[42],"methods":[43],"require":[45,65],"large":[46,75],"amounts":[47],"annotated":[49],"some":[52],"sense.":[53],"Self-supervised":[54],"learning":[55,90],"paradigm,":[56],"aims":[58],"to":[59,163,182,212,231,236,240],"train":[60],"models":[61],"that":[62,85,234],"do":[63],"not":[64],"any":[66],"samples":[68,199,202,205,233],"can":[69],"make":[70],"full":[71],"use":[72],"unlabeled":[78],"samples.":[80],"But":[81],"drawback":[83],"is":[84,99,162,181],"they":[86],"fall":[87],"short":[88],"class":[91,219,239],"discriminative":[92],"sample":[93,209,229],"representations":[94],"since":[95],"no":[96],"information":[98],"utilized":[100],"during":[101],"training.":[102],"To":[103],"solve":[104],"above":[106],"problem,":[107],"we":[108],"propose":[109],"semi-supervised":[111],"model,":[112],"named":[113],"consistency":[114,152],"regularization":[115,153],"enhanced":[116],"graph":[117,142,186],"attention":[118],"network":[119],"(CR-GAT)":[120],"for":[121],"recognition.":[124],"The":[125,178,193],"CR-GAT":[126],"mainly":[127],"consists":[128],"three":[130,249],"modules,":[131],"namely":[132],"feature":[134,141,191],"extraction":[135],"and":[136,144,168,203],"fusion":[137],"(FEF)":[138],"module,":[139,195],"building":[143],"augment":[145],"(GBA)":[146],"module":[147,180],"as":[148,150],"well":[149],"(CR)":[154],"module.":[155],"Specifically,":[156],"t":[157],"he":[158],"F":[159],"EFm":[160],"odule":[161],"extract":[164],"task-specific":[165],"features":[167,173],"highlight":[169],"most":[171,259],"valuable":[172],"from":[174,200,206,221],"signals.":[177],"GBA":[179],"build":[183],"sample-related":[185],"representation":[187],"set.":[192],"CR":[194],"draws":[197],"support":[198],"anchor":[204],"entire":[208],"set,":[210],"intends":[211],"minimize":[213],"difference":[215],"between":[216],"predicted":[218],"distributions":[220],"different":[222],"graphs":[223],"constructed":[224],"by":[225],"multi-views":[226],"set":[230],"push":[232],"belong":[235],"same":[238],"be":[241],"grouped":[242],"together.":[243],"We":[244],"conduct":[245],"our":[246],"experiment":[247],"on":[248],"real-world":[250],"datasets,":[251],"experimental":[253],"results":[254],"show":[255],"method":[257],"surpasses":[258],"competitive":[261],"models.":[262]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
