{"id":"https://openalex.org/W2789476480","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302098","title":"Three class emotions recognition based on deep learning using staked autoencoder","display_name":"Three class emotions recognition based on deep learning using staked autoencoder","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2789476480","doi":"https://doi.org/10.1109/cisp-bmei.2017.8302098","mag":"2789476480"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2017.8302098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5101691668","display_name":"Banghua Yang","orcid":"https://orcid.org/0000-0002-8561-5631"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Banghua Yang","raw_affiliation_strings":["Department of Automation, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102012659","display_name":"Xu Han","orcid":"https://orcid.org/0000-0003-3491-8576"},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Han","raw_affiliation_strings":["Department of Automation, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056093246","display_name":"Jianzhen Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I113940042","display_name":"Shanghai University","ror":"https://ror.org/006teas31","country_code":"CN","type":"education","lineage":["https://openalex.org/I113940042"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianzhen Tang","raw_affiliation_strings":["Department of Automation, Shanghai University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Shanghai University, Shanghai, China","institution_ids":["https://openalex.org/I113940042"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101691668"],"corresponding_institution_ids":["https://openalex.org/I113940042"],"apc_list":null,"apc_paid":null,"fwci":2.1762,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.88186373,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9976000189781189,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9649999737739563,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/autoencoder","display_name":"Autoencoder","score":0.9544987678527832},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.938584566116333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7416809797286987},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6407746076583862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6366478800773621},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6283174157142639},{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.5153512358665466},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4776664674282074},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4681226313114166},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.4632362723350525},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4276777505874634},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4127953052520752},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09446483850479126}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.9544987678527832},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.938584566116333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7416809797286987},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6407746076583862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6366478800773621},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6283174157142639},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.5153512358665466},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4776664674282074},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4681226313114166},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4632362723350525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4276777505874634},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4127953052520752},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09446483850479126},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2017.8302098","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2017.8302098","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W304081852","https://openalex.org/W1827297289","https://openalex.org/W1932469787","https://openalex.org/W1938425378","https://openalex.org/W1947251450","https://openalex.org/W1970727126","https://openalex.org/W1971835160","https://openalex.org/W2002565627","https://openalex.org/W2013247247","https://openalex.org/W2110798204","https://openalex.org/W2132889650","https://openalex.org/W2134602648","https://openalex.org/W2145094598","https://openalex.org/W2158411555","https://openalex.org/W2170077112","https://openalex.org/W2232827647","https://openalex.org/W2239639518","https://openalex.org/W2480675886","https://openalex.org/W2540556213","https://openalex.org/W2953301748","https://openalex.org/W2997574889","https://openalex.org/W3136022601","https://openalex.org/W6638685980","https://openalex.org/W6640737715","https://openalex.org/W6642716067","https://openalex.org/W6651365498","https://openalex.org/W6679863951","https://openalex.org/W6679967756","https://openalex.org/W6681096077","https://openalex.org/W6683335406","https://openalex.org/W6729143965","https://openalex.org/W6791837114"],"related_works":["https://openalex.org/W3107204728","https://openalex.org/W2769441402","https://openalex.org/W2594436708","https://openalex.org/W4360994128","https://openalex.org/W3086240734","https://openalex.org/W2736305774","https://openalex.org/W2951850672","https://openalex.org/W2789476480","https://openalex.org/W3120400911","https://openalex.org/W4321789545"],"abstract_inverted_index":{"Emotion":[0,56],"recognition":[1],"is":[2,12,105,156,176],"a":[3,31,48],"hot":[4],"spot":[5],"in":[6,16,91,99,126],"advanced":[7],"humancomputer":[8],"interaction":[9],"system,":[10],"which":[11],"of":[13,39,79,123,135,151],"great":[14],"significance":[15],"artificial":[17],"intelligence,":[18],"health":[19],"care,":[20],"distance":[21],"education,":[22],"military":[23],"field":[24],"and":[25,47,115,131,162],"so":[26],"on.":[27],"The":[28,102,117,164],"paper":[29],"builds":[30],"stacked":[32],"autoencoder":[33,44,63,75,90],"deep":[34],"learning":[35],"classification":[36,149],"network":[37,98,104],"consist":[38],"an":[40],"input":[41],"layer,":[42],"two":[43],"hidden":[45],"layers":[46],"softmax":[49,86],"classifier":[50],"output":[51,78],"layer":[52],"based":[53],"on":[54],"SJTU":[55],"EEG":[57,124,137],"Dataset":[58],"(SEED).":[59],"Pretrain":[60,88],"the":[61,68,73,77,85,96,132,147,167],"first":[62,80],"employed":[64],"L-BFGS":[65],"to":[66,84,107,179],"optimize":[67],"cost":[69],"function.":[70],"Then":[71],"pretrain":[72],"second":[74],"with":[76,143],"autoencoder.":[81],"Finally":[82],"send":[83],"classifier.":[87],"each":[89],"forward":[92],"propagation,":[93],"then":[94],"fine-tuning":[95],"whole":[97,136],"back":[100],"propagation.":[101],"well-trained":[103],"used":[106],"classify":[108],"three":[109,152],"emotion":[110,154,180],"states":[111,155],"including":[112],"happy,":[113],"neural":[114],"grief.":[116],"raw":[118],"inputs":[119],"are":[120,141],"differential":[121,133,171],"entropy":[122,134,172],"signal":[125],"five":[127],"rhythmic":[128],"frequencies":[129],"band":[130,170,175],"signal.":[138],"Fourteen":[139],"experiments":[140],"performed":[142],"5-fold":[144],"cross":[145],"validation,":[146],"average":[148],"accuracy":[150],"class":[153],"59.6%,":[157],"66.27%,":[158],"71.97%,":[159],"78.48%,":[160],"82.56%":[161],"85.5%.":[163],"result":[165],"shows":[166],"higher":[168],"frequency":[169],"like":[173],"Gamma":[174],"more":[177],"relative":[178],"reaction.":[181]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}