{"id":"https://openalex.org/W4401072328","doi":"https://doi.org/10.1109/memea60663.2024.10596800","title":"Autoencoder Based Nonlinear Feature Extraction from EDA Signals for Emotion Recognition","display_name":"Autoencoder Based Nonlinear Feature Extraction from EDA Signals for Emotion Recognition","publication_year":2024,"publication_date":"2024-06-26","ids":{"openalex":"https://openalex.org/W4401072328","doi":"https://doi.org/10.1109/memea60663.2024.10596800"},"language":"en","primary_location":{"id":"doi:10.1109/memea60663.2024.10596800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea60663.2024.10596800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","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/A5017507506","display_name":"Yedukondala Rao Veeranki","orcid":"https://orcid.org/0000-0002-7904-7543"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yedukondala Rao Veeranki","raw_affiliation_strings":["Biomedical Engineering, University of Connecticut,Storrs,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, University of Connecticut,Storrs,USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062261441","display_name":"Lu\u00eds Roberto Mercado D\u00edaz","orcid":"https://orcid.org/0000-0003-3543-3677"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Luis R. Mercado-Diaz","raw_affiliation_strings":["Biomedical Engineering, University of Connecticut,Storrs,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, University of Connecticut,Storrs,USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012375687","display_name":"Hugo F. Posada\u2013Quintero","orcid":"https://orcid.org/0000-0003-4514-4772"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hugo F. Posada-Quintero","raw_affiliation_strings":["Biomedical Engineering, University of Connecticut,Storrs,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, University of Connecticut,Storrs,USA","institution_ids":["https://openalex.org/I140172145"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I140172145"],"apc_list":null,"apc_paid":null,"fwci":0.5972,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72743417,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"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/T10320","display_name":"Neural Networks and Applications","score":0.6373999714851379,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.6373999714851379,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.891859769821167},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7524540424346924},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.682314932346344},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6706550121307373},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6430136561393738},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.46278268098831177},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44797012209892273},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2062877118587494}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.891859769821167},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7524540424346924},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.682314932346344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6706550121307373},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6430136561393738},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.46278268098831177},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44797012209892273},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2062877118587494},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/memea60663.2024.10596800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea60663.2024.10596800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1239497932","https://openalex.org/W2002055708","https://openalex.org/W2008264338","https://openalex.org/W2045234652","https://openalex.org/W2093540104","https://openalex.org/W2144408839","https://openalex.org/W2149628368","https://openalex.org/W2164368909","https://openalex.org/W2170279144","https://openalex.org/W2276264306","https://openalex.org/W2329439512","https://openalex.org/W2504069552","https://openalex.org/W2524142780","https://openalex.org/W2572977003","https://openalex.org/W2620233741","https://openalex.org/W2735557231","https://openalex.org/W2757888873","https://openalex.org/W2891385203","https://openalex.org/W2911964244","https://openalex.org/W2917094047","https://openalex.org/W2946526173","https://openalex.org/W2953127026","https://openalex.org/W3000402017","https://openalex.org/W3008432199","https://openalex.org/W3165586548","https://openalex.org/W4200556833","https://openalex.org/W4293235175","https://openalex.org/W4298149657","https://openalex.org/W4323653672","https://openalex.org/W4391128446","https://openalex.org/W6681082973"],"related_works":["https://openalex.org/W4386159726","https://openalex.org/W2601157893","https://openalex.org/W2131735617","https://openalex.org/W2373006798","https://openalex.org/W2056912418","https://openalex.org/W2123759770","https://openalex.org/W2033213769","https://openalex.org/W4312376745","https://openalex.org/W2136016640","https://openalex.org/W2082269393"],"abstract_inverted_index":{"The":[0],"field":[1],"of":[2,15,44,81,95,120,132,163,173],"emotion":[3,190],"recognition":[4,191],"plays":[5],"a":[6,48,70,102],"pivotal":[7],"role":[8],"in":[9],"enhancing":[10],"the":[11,38,79,92,108,117,124,130,133,161,171],"quality":[12],"and":[13,41,156,165,176,192],"effectiveness":[14],"human-computer":[16,199],"interaction.":[17],"Electrodermal":[18],"Activity":[19],"(EDA)":[20],"signals,":[21],"which":[22],"reflect":[23],"autonomic":[24],"nervous":[25],"system":[26],"response,":[27],"have":[28,60],"been":[29],"widely":[30],"employed":[31,128],"to":[32,114,179],"understand":[33],"user":[34],"emotional":[35,183],"states.":[36,184],"However,":[37],"inherent":[39],"nonlinear":[40],"non-stationary":[42],"nature":[43],"these":[45,56],"signals":[46,97],"poses":[47],"challenge":[49],"for":[50,78,129,160,195],"traditional":[51],"analysis":[52],"methods.":[53,66],"To":[54],"address":[55],"challenges,":[57],"recent":[58],"studies":[59],"focused":[61],"on":[62],"non-linear":[63,82,118],"signal":[64],"processing":[65],"This":[67,185],"work":[68,186],"proposes":[69],"novel":[71],"approach":[72,175],"utilizing":[73],"deep":[74],"learning":[75,147],"based":[76],"Autoencoders":[77,113],"extraction":[80],"features":[83,111,141],"from":[84,101],"EDA":[85,96,121],"signals.":[86,122],"For":[87],"this,":[88],"first,":[89],"we":[90,106],"estimate":[91],"phasic":[93],"component":[94],"that":[98],"are":[99,142],"obtained":[100],"public":[103],"dataset.":[104],"Then":[105],"compute":[107],"64":[109],"bottleneck":[110],"using":[112],"effectively":[115],"capture":[116],"characteristics":[119],"Further,":[123],"RF-RFE":[125],"method":[126],"is":[127],"selection":[131],"top":[134],"five":[135],"most":[136],"informative":[137],"features.":[138],"These":[139],"selected":[140],"then":[143],"fed":[144],"into":[145],"machine":[146],"algorithms":[148],"including":[149],"random":[150],"Forest,":[151],"support":[152],"vector":[153],"machine,":[154],"Adaboost,":[155],"extreme":[157],"gradient":[158],"boosting":[159],"classification":[162],"arousal":[164],"valence":[166],"dimensions.":[167],"Our":[168],"results":[169],"demonstrate":[170],"robustness":[172],"this":[174],"its":[177],"ability":[178],"accurately":[180],"discriminate":[181],"complex":[182],"significantly":[187],"advances":[188],"EDA-based":[189],"opens":[193],"avenues":[194],"more":[196],"emotionally":[197],"attuned":[198],"interaction":[200],"systems.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}