{"id":"https://openalex.org/W2574833558","doi":"https://doi.org/10.1109/mmsp.2016.7813351","title":"Affective states classification using EEG and semi-supervised deep learning approaches","display_name":"Affective states classification using EEG and semi-supervised deep learning approaches","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2574833558","doi":"https://doi.org/10.1109/mmsp.2016.7813351","mag":"2574833558"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2016.7813351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2016.7813351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"},"type":"article","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/A5115781212","display_name":"Haiyan Xu","orcid":"https://orcid.org/0000-0001-5600-6557"},"institutions":[{"id":"https://openalex.org/I75717288","display_name":"Rogers (United States)","ror":"https://ror.org/05m9vrv91","country_code":"US","type":"company","lineage":["https://openalex.org/I75717288"]},{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA","US"],"is_corresponding":true,"raw_author_name":"Haiyan Xu","raw_affiliation_strings":["The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CANADA"],"affiliations":[{"raw_affiliation_string":"The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CANADA","institution_ids":["https://openalex.org/I75717288","https://openalex.org/I185261750"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059152392","display_name":"Konstantinos N. Plataniotis","orcid":"https://orcid.org/0000-0003-3647-5473"},"institutions":[{"id":"https://openalex.org/I75717288","display_name":"Rogers (United States)","ror":"https://ror.org/05m9vrv91","country_code":"US","type":"company","lineage":["https://openalex.org/I75717288"]},{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA","US"],"is_corresponding":false,"raw_author_name":"Konstantinos N. Plataniotis","raw_affiliation_strings":["The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CANADA"],"affiliations":[{"raw_affiliation_string":"The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, CANADA","institution_ids":["https://openalex.org/I75717288","https://openalex.org/I185261750"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5115781212"],"corresponding_institution_ids":["https://openalex.org/I185261750","https://openalex.org/I75717288"],"apc_list":null,"apc_paid":null,"fwci":4.0053,"has_fulltext":false,"cited_by_count":82,"citation_normalized_percentile":{"value":0.94142436,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"2016","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9994000196456909,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9858999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.721487820148468},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6981154084205627},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.6680908203125},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.612491250038147},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5914732217788696},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5450206398963928},{"id":"https://openalex.org/keywords/affective-computing","display_name":"Affective computing","score":0.5432693958282471},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5011560916900635},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.48635464906692505},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.47910359501838684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46811074018478394},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45001450181007385},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44116368889808655},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43661078810691833},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.4158038794994354},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34946513175964355},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1419558823108673},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.09245461225509644}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.721487820148468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6981154084205627},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.6680908203125},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.612491250038147},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5914732217788696},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5450206398963928},{"id":"https://openalex.org/C6438553","wikidata":"https://www.wikidata.org/wiki/Q1185804","display_name":"Affective computing","level":2,"score":0.5432693958282471},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5011560916900635},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.48635464906692505},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.47910359501838684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46811074018478394},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45001450181007385},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44116368889808655},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43661078810691833},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.4158038794994354},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34946513175964355},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1419558823108673},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.09245461225509644},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mmsp.2016.7813351","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2016.7813351","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"},{"id":"mag:2747927976","is_oa":false,"landing_page_url":"http://jglobal.jst.go.jp/en/public/20090422/201702244025169672","pdf_url":null,"source":{"id":"https://openalex.org/S4306512817","display_name":"IEEE Conference Proceedings","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":null,"is_accepted":false,"is_published":null,"raw_source_name":"IEEE Conference Proceedings","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W210506677","https://openalex.org/W2002055708","https://openalex.org/W2004104731","https://openalex.org/W2028824762","https://openalex.org/W2043572211","https://openalex.org/W2065250073","https://openalex.org/W2075126953","https://openalex.org/W2118978333","https://openalex.org/W2132889650","https://openalex.org/W2136922672","https://openalex.org/W2139564752","https://openalex.org/W2145094598","https://openalex.org/W2148603752","https://openalex.org/W2149628368","https://openalex.org/W2153635508","https://openalex.org/W2164700406","https://openalex.org/W2164985412","https://openalex.org/W2997574889","https://openalex.org/W3120421331","https://openalex.org/W6608523365","https://openalex.org/W6681096077","https://openalex.org/W7071374342"],"related_works":["https://openalex.org/W4367312605","https://openalex.org/W2967180365","https://openalex.org/W3023111453","https://openalex.org/W2786830879","https://openalex.org/W4310841718","https://openalex.org/W4296437653","https://openalex.org/W560265342","https://openalex.org/W4360994129","https://openalex.org/W4313351211","https://openalex.org/W3125908420"],"abstract_inverted_index":{"Affective":[0],"states":[1,45,91,124,142,175],"of":[2,61,68,75,84,100,134,165,207,215,227,232],"a":[3,42,73,139,224],"user":[4],"provide":[5],"important":[6],"information":[7,14],"for":[8,121,170],"many":[9],"applications":[10],"such":[11,96],"as,":[12],"personalized":[13],"(e.g.,":[15],"multimedia":[16],"content)":[17],"retrieval/delivery":[18],"or":[19,49,52,204],"intelligent":[20],"human-computer":[21],"interface":[22],"design.":[23],"In":[24],"recently":[25],"years,":[26],"physiological":[27],"signals,":[28],"Electroencephalogram":[29],"(EEG)":[30],"in":[31,40,238],"particular,":[32],"have":[33],"been":[34],"shown":[35],"to":[36,57,152,198],"be":[37],"very":[38],"effective":[39],"estimating":[41],"user's":[43],"affective":[44,90,123,141],"during":[46],"social":[47],"interaction":[48],"under":[50],"video":[51],"audio":[53],"stimuli.":[54],"However,":[55],"due":[56],"the":[58,65,78,85,89,122,132,135,149,182,188,202,208,217,233],"large":[59],"number":[60],"parameters":[62],"associated":[63],"with":[64],"neural":[66],"expression":[67],"emotion,":[69],"there":[70],"is":[71,221],"still":[72],"lot":[74],"unknowns":[76],"on":[77,95,148,201],"specific":[79,118],"spatial":[80,203],"and":[81,88,109,168,173],"spectral":[82,205],"correlation":[83],"EEG":[86,128],"signal":[87],"expression.":[92],"To":[93,130],"investigate":[94],"correlation,":[97],"two":[98],"types":[99],"semi-supervised":[101,137,218],"deep":[102,110],"learning":[103,219],"approaches,":[104,138],"stacked":[105],"denoising":[106],"autoencoder":[107],"(SDAE)":[108],"belief":[111],"networks":[112],"(DBN),":[113],"were":[114,145,195,236],"applied":[115],"as":[116],"application":[117],"feature":[119],"extractors":[120],"classification":[125,143,176,184],"problem":[126],"using":[127],"signals.":[129],"evaluate":[131],"efficacy":[133],"proposed":[136],"subject-specific":[140],"experiment":[144],"carried":[146],"out":[147],"DEAP":[150],"database":[151],"classify":[153],"2-dimensional":[154],"affect":[155],"states.":[156],"The":[157],"DBN":[158],"based":[159],"model":[160],"achieved":[161],"averaged":[162],"F1":[163],"scores":[164],"86.67%,":[166],"86.60%":[167],"86.69%":[169],"arousal,":[171],"valence":[172],"liking":[174],"respectively,":[177],"which":[178],"has":[179],"significantly":[180],"improved":[181],"state-of-art":[183],"performance.":[185],"By":[186],"examining":[187],"weight":[189],"vectors":[190],"at":[191],"each":[192],"layer,":[193],"we":[194],"also":[196],"able":[197],"gain":[199],"insights":[200],"locations":[206],"most":[209],"discriminating":[210],"features.":[211],"Another":[212],"main":[213],"advantage":[214],"applying":[216],"methods":[220],"that":[222],"only":[223],"small":[225],"fraction":[226],"labeled":[228],"data,":[229],"e.g.,":[230],"1/6":[231],"training":[234],"samples,":[235],"used":[237],"this":[239],"study.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":12},{"year":2018,"cited_by_count":9},{"year":2017,"cited_by_count":3}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}