{"id":"https://openalex.org/W4386150381","doi":"https://doi.org/10.48550/arxiv.2308.11635","title":"Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition","display_name":"Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition","publication_year":2023,"publication_date":"2023-08-13","ids":{"openalex":"https://openalex.org/W4386150381","doi":"https://doi.org/10.48550/arxiv.2308.11635"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2308.11635","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.11635","pdf_url":"https://arxiv.org/pdf/2308.11635","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2308.11635","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020197517","display_name":"Weishan Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ye, Weishan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363345","display_name":"Zhiguo Zhang","orcid":"https://orcid.org/0000-0001-7992-7965"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhiguo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101573996","display_name":"Fei Teng","orcid":"https://orcid.org/0000-0003-1447-3450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Teng, Fei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100402911","display_name":"Min Zhang","orcid":"https://orcid.org/0000-0002-3895-5510"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Min","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100658372","display_name":"Jianhong Wang","orcid":"https://orcid.org/0000-0002-7375-8387"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jianhong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059641845","display_name":"Dong Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Dong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Li, Fali","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Fali","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Xu, Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Peng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036446937","display_name":"Zhen Liang","orcid":"https://orcid.org/0000-0002-1749-2975"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Zhen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5020197517"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9977999925613403,"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":0.9977999925613403,"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":0.9968000054359436,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9485999941825867,"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/computer-science","display_name":"Computer science","score":0.7355379462242126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6571603417396545},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6442873477935791},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5760133266448975},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5027587413787842},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.470692902803421},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46591877937316895},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4590950906276703},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4235283434391022},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.4217036962509155},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3704991936683655},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3232662081718445},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13078951835632324},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.07796022295951843}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7355379462242126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571603417396545},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6442873477935791},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5760133266448975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5027587413787842},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.470692902803421},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46591877937316895},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4590950906276703},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4235283434391022},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.4217036962509155},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3704991936683655},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3232662081718445},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13078951835632324},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.07796022295951843},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2308.11635","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.11635","pdf_url":"https://arxiv.org/pdf/2308.11635","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2308.11635","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2308.11635","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2308.11635","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2308.11635","pdf_url":"https://arxiv.org/pdf/2308.11635","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4662639773","display_name":null,"funder_award_id":"62071310","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5871031305","display_name":null,"funder_award_id":"82272114","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8636691261","display_name":null,"funder_award_id":"2022SHIBS0003","funder_id":"https://openalex.org/F4320330381","funder_display_name":"Shenzhen-Hong Kong Institute of Brain Science"},{"id":"https://openalex.org/G8879676990","display_name":null,"funder_award_id":"62276169","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325571","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70"},{"id":"https://openalex.org/F4320328618","display_name":"Lingnan University","ror":"https://ror.org/0563pg902"},{"id":"https://openalex.org/F4320330381","display_name":"Shenzhen-Hong Kong Institute of Brain Science","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4386150381.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4285218279","https://openalex.org/W3204853141"],"abstract_inverted_index":{"Electroencephalography":[0],"(EEG)":[1],"is":[2,48,129],"an":[3,194],"objective":[4],"tool":[5],"for":[6,70,131],"emotion":[7,31,61,137,217],"recognition":[8],"with":[9],"promising":[10],"applications.":[11],"However,":[12],"the":[13,26,52,151,159,182,210],"scarcity":[14,212],"of":[15,29,54,197],"labeled":[16,56,91,152],"data":[17,57,148],"remains":[18],"a":[19,36,81,105,121,125,173],"major":[20],"challenge":[21,53],"in":[22,58,120,150,208,214],"this":[23,34],"field,":[24],"limiting":[25],"widespread":[27],"use":[28],"EEG-based":[30,60,216],"recognition.":[32,62,218],"In":[33],"paper,":[35],"semi-supervised":[37,82,122,174],"Dual-stream":[38],"Self-Attentive":[39],"Adversarial":[40],"Graph":[41],"Contrastive":[42],"learning":[43,108],"framework":[44,65],"(termed":[45],"as":[46],"DS-AGC)":[47],"proposed":[49,183],"to":[50,86,110,145,158],"tackle":[51],"limited":[55],"cross-subject":[59,175,215],"The":[63,77,101],"DS-AGC":[64],"includes":[66],"two":[67,166],"parallel":[68],"streams":[69],"extracting":[71],"non-structural":[72,78],"and":[73,97,136,147,170,201],"structural":[74,102],"EEG":[75,118,141],"features.":[76],"stream":[79,103],"incorporates":[80],"multi-domain":[83],"adaptation":[84],"method":[85,109],"alleviate":[87],"distribution":[88],"discrepancy":[89],"among":[90],"source":[92,95,153],"domain,":[93,96],"unlabeled":[94],"unknown":[98],"target":[99,160],"domain.":[100,161],"develops":[104],"graph":[106],"contrastive":[107],"extract":[111],"effective":[112],"graph-based":[113],"feature":[114,132],"representation":[115],"from":[116],"multiple":[117],"channels":[119],"manner.":[123],"Further,":[124],"self-attentive":[126],"fusion":[127],"module":[128],"developed":[130],"fusion,":[133],"sample":[134],"selection,":[135],"recognition,":[138],"which":[139],"highlights":[140],"features":[142],"more":[143],"relevant":[144],"emotions":[146],"samples":[149],"domain":[154],"that":[155,181],"are":[156],"closer":[157],"Extensive":[162],"experiments":[163],"conducted":[164],"on":[165,199,203],"benchmark":[167],"databases":[168],"(SEED":[169],"SEED-IV)":[171],"using":[172],"leave-one-subject-out":[176],"cross-validation":[177],"evaluation":[178],"scheme":[179],"show":[180],"model":[184],"outperforms":[185],"existing":[186],"methods":[187],"under":[188],"different":[189],"incomplete":[190],"label":[191,211],"conditions":[192],"(with":[193],"average":[195],"improvement":[196],"5.83%":[198],"SEED":[200],"6.99%":[202],"SEED-IV),":[204],"demonstrating":[205],"its":[206],"effectiveness":[207],"addressing":[209],"problem":[213]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
