{"id":"https://openalex.org/W4416781899","doi":"https://doi.org/10.1016/j.bspc.2025.109223","title":"Real-time EEG-based emotion recognition using Riemannian quantification learning","display_name":"Real-time EEG-based emotion recognition using Riemannian quantification learning","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W4416781899","doi":"https://doi.org/10.1016/j.bspc.2025.109223"},"language":"en","primary_location":{"id":"doi:10.1016/j.bspc.2025.109223","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109223","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.bspc.2025.109223","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026748241","display_name":"Bo Lv","orcid":"https://orcid.org/0000-0001-9304-4077"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Lv","raw_affiliation_strings":["State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China","institution_ids":["https://openalex.org/I142078773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045740832","display_name":"Yaobo Yu","orcid":"https://orcid.org/0000-0003-4327-3626"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaobo Yu","raw_affiliation_strings":["State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China","institution_ids":["https://openalex.org/I142078773"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111216124","display_name":"Fanbo Zhuo","orcid":null},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fanbo Zhuo","raw_affiliation_strings":["State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China","institution_ids":["https://openalex.org/I142078773"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065849358","display_name":"Fengzhen Tang","orcid":"https://orcid.org/0000-0002-4654-9440"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengzhen Tang","raw_affiliation_strings":["State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, PR China","institution_ids":["https://openalex.org/I142078773"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065849358"],"corresponding_institution_ids":["https://openalex.org/I142078773"],"apc_list":{"value":2420,"currency":"USD","value_usd":2420},"apc_paid":{"value":2420,"currency":"USD","value_usd":2420},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38469871,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"114","issue":null,"first_page":"109223","last_page":"109223"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.7020000219345093,"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":0.7020000219345093,"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":0.28450000286102295,"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/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.000699999975040555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6388000249862671},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5400000214576721},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5072000026702881},{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"Emotion recognition","score":0.5037000179290771},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4823000133037567},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.439300000667572},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.41290000081062317},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.41100001335144043},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.3984000086784363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6935999989509583},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6388000249862671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5942999720573425},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5400000214576721},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5072000026702881},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4823000133037567},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.439300000667572},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4260999858379364},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.41100001335144043},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4002000093460083},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.3984000086784363},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.38600000739097595},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.3806000053882599},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3276999890804291},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C2780102126","wikidata":"https://www.wikidata.org/wiki/Q10928179","display_name":"Online and offline","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2531000077724457}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.bspc.2025.109223","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109223","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.bspc.2025.109223","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.bspc.2025.109223","pdf_url":null,"source":{"id":"https://openalex.org/S8427965","display_name":"Biomedical Signal Processing and Control","issn_l":"1746-8094","issn":["1746-8094","1746-8108"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Biomedical Signal Processing and Control","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1493605502","display_name":null,"funder_award_id":"2024-BSBA-50","funder_id":"https://openalex.org/F4320326183","funder_display_name":"Doctoral Start-up Foundation of Liaoning Province"},{"id":"https://openalex.org/G3174830098","display_name":null,"funder_award_id":"RC231112","funder_id":"https://openalex.org/F4320336621","funder_display_name":"Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program"},{"id":"https://openalex.org/G4424311558","display_name":null,"funder_award_id":"62273335","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5769891676","display_name":null,"funder_award_id":"XLYC2403184","funder_id":"https://openalex.org/F4320329895","funder_display_name":"Liaoning Revitalization Talents Program"},{"id":"https://openalex.org/G6910179801","display_name":null,"funder_award_id":"2024021112-JH3/102","funder_id":"https://openalex.org/F4320323086","funder_display_name":"Natural Science Foundation of Liaoning Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null},{"id":"https://openalex.org/F4320326183","display_name":"Doctoral Start-up Foundation of Liaoning Province","ror":null},{"id":"https://openalex.org/F4320329895","display_name":"Liaoning Revitalization Talents Program","ror":null},{"id":"https://openalex.org/F4320336621","display_name":"Shenyang Young and Middle-aged Science and Technology Innovation Talent Support Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1929727337","https://openalex.org/W1947251450","https://openalex.org/W1983496390","https://openalex.org/W2048192550","https://openalex.org/W2109993655","https://openalex.org/W2114429947","https://openalex.org/W2167716931","https://openalex.org/W2555065233","https://openalex.org/W2580887161","https://openalex.org/W2614527022","https://openalex.org/W2786768213","https://openalex.org/W2913846632","https://openalex.org/W2949122071","https://openalex.org/W2962905870","https://openalex.org/W3004735003","https://openalex.org/W3012897833","https://openalex.org/W3082367983","https://openalex.org/W3121386809","https://openalex.org/W3131775586","https://openalex.org/W3159301005","https://openalex.org/W3205895886","https://openalex.org/W4210437389","https://openalex.org/W4225411558","https://openalex.org/W4226213590","https://openalex.org/W4282975966","https://openalex.org/W4315778234","https://openalex.org/W4318815957","https://openalex.org/W4319990494","https://openalex.org/W4323644177","https://openalex.org/W4323870229","https://openalex.org/W4366778147","https://openalex.org/W4379615104","https://openalex.org/W4388966806","https://openalex.org/W4389105052","https://openalex.org/W4390884206","https://openalex.org/W4391774550","https://openalex.org/W4399449629","https://openalex.org/W4403917837","https://openalex.org/W4405893635"],"related_works":[],"abstract_inverted_index":{"Emotion":[0],"recognition":[1,230],"via":[2],"electroencephalogram":[3],"(EEG)":[4],"has":[5],"obtained":[6],"significant":[7],"attention":[8],"in":[9,23,177,288],"many":[10],"fields,":[11],"including":[12],"brain-computer":[13],"interfaces.":[14],"It":[15],"is":[16,225,248,259,286],"crucial":[17],"to":[18,38,45,71,185,250],"accurately":[19],"identify":[20],"emotional":[21,50],"states":[22],"real-time":[24,228],"scenarios":[25],"with":[26,65,87,207,231],"few-channel":[27],"EEG":[28,51,233,269],"signals":[29],"and":[30,90,101,136,155,171,235,271],"minimal":[31,236],"user":[32],"training":[33,91,141,176,237,272],"data.":[34],"This":[35],"study":[36],"tries":[37],"use":[39],"symmetric":[40],"positive":[41],"definite":[42],"(SPD)":[43],"matrices":[44],"capture":[46],"the":[47,66,73,83,107,111,127,163,167,178,208,222,240,245,251,283],"characteristics":[48],"of":[49,140,175,268,282],"signals.":[52],"We":[53,160],"propose":[54],"a":[55,137,172,203],"classifier,":[56],"termed":[57],"as":[58],"Riemannian":[59,257],"robust":[60],"soft":[61],"learning":[62,69],"vector":[63],"quantization":[64],"log-Euclidean":[67],"metric":[68],"(RRSLVQ-LEML),":[70],"distinguish":[72],"SPD":[74,112],"matrices.":[75],"To":[76],"provide":[77],"thorough":[78],"analyses,":[79],"we":[80],"first":[81],"evaluated":[82,243],"offline":[84,209,241],"classification":[85,124,195],"performance":[86,242],"channel":[88,169],"selection":[89],"data":[92],"reduction":[93],"across":[94,276],"three":[95,128,277],"public":[96],"datasets,":[97],"i.e.,":[98],"SEED,":[99],"SEED-IV,":[100],"SEED-V.":[102],"The":[103,123,192,266,280],"results":[104,210],"demonstrated":[105],"that":[106,221],"proposed":[108,164,284],"model":[109,165,224,247],"outperformed":[110],"network":[113],"(SPDnet)":[114],"significantly":[115],"(":[116,211],"p":[117],"<":[118],"0":[119,214],".":[120,215],"05":[121],").":[122,217],"accuracies":[125],"on":[126],"datasets":[129],"using":[130],"selected":[131,168],"universal":[132],"channels":[133,234,270],"(22":[134],"channels)":[135],"limited":[138],"number":[139,267],"samples":[142,144,273],"(150":[143],"for":[145,188,227,261],"each":[146],"category)":[147],"were":[148],"88.33%":[149],"\u00b1":[150,153,157,199],"5.77%,":[151],"76.05%":[152],"7.85%,":[154],"70.62%":[156],"11.66%,":[158],"respectively.":[159],"further":[161],"validated":[162,287],"under":[166],"setup":[170],"small":[173],"amount":[174],"online":[179,194,252,290],"scenario":[180],"by":[181,244],"recruiting":[182],"seven":[183],"participants":[184],"conduct":[186],"experiments":[187],"four-category":[189],"emotion":[190,229,263],"recognition.":[191,264],"average":[193],"accuracy":[196],"was":[197,274],"77.28%":[198],"6.68%,":[200],"which":[201],"exhibited":[202],"strong":[204],"linear":[205],"correlation":[206],"r":[212],"=":[213],"82":[216],"These":[218],"findings":[219],"indicate":[220],"RRSLVQ-LEML":[223,246],"suitable":[226],"few":[232],"samples.":[238],"Furthermore,":[239],"transferable":[249],"scenario.":[253,291],"\u2022":[254,265,279],"A":[255],"novel":[256],"classifier":[258],"utilized":[260],"EEG-based":[262],"optimized":[275],"datasets.":[278],"effectiveness":[281],"approach":[285],"an":[289]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-28T00:00:00"}
