{"id":"https://openalex.org/W4391424235","doi":"https://doi.org/10.1109/metroxraine58569.2023.10405681","title":"Emotion Personalization with Machine Learning using EEG Signals and Dry Electrodes","display_name":"Emotion Personalization with Machine Learning using EEG Signals and Dry Electrodes","publication_year":2023,"publication_date":"2023-10-25","ids":{"openalex":"https://openalex.org/W4391424235","doi":"https://doi.org/10.1109/metroxraine58569.2023.10405681"},"language":"en","primary_location":{"id":"doi:10.1109/metroxraine58569.2023.10405681","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine58569.2023.10405681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","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/A5031614032","display_name":"Hamza Amrani","orcid":"https://orcid.org/0000-0003-2089-8033"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Hamza Amrani","raw_affiliation_strings":["System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126"],"affiliations":[{"raw_affiliation_string":"System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126","institution_ids":["https://openalex.org/I66752286"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015645148","display_name":"Daniela Micucci","orcid":"https://orcid.org/0000-0003-1261-2234"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Daniela Micucci","raw_affiliation_strings":["System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126"],"affiliations":[{"raw_affiliation_string":"System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126","institution_ids":["https://openalex.org/I66752286"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007968543","display_name":"Marco Nalin","orcid":"https://orcid.org/0000-0002-9957-3225"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marco Nalin","raw_affiliation_strings":["ab medica s.p.a., Cerro Maggiore,Milano,Italy,20023"],"affiliations":[{"raw_affiliation_string":"ab medica s.p.a., Cerro Maggiore,Milano,Italy,20023","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053344554","display_name":"Paolo Napoletano","orcid":"https://orcid.org/0000-0001-9112-0574"},"institutions":[{"id":"https://openalex.org/I66752286","display_name":"University of Milano-Bicocca","ror":"https://ror.org/01ynf4891","country_code":"IT","type":"education","lineage":["https://openalex.org/I66752286"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paolo Napoletano","raw_affiliation_strings":["System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126"],"affiliations":[{"raw_affiliation_string":"System and Communication, University of Milano-Bicocca,Department of Informatics,Milano,Italy,20126","institution_ids":["https://openalex.org/I66752286"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5031614032"],"corresponding_institution_ids":["https://openalex.org/I66752286"],"apc_list":null,"apc_paid":null,"fwci":0.181,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53177123,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"132","last_page":"137"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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":1.0,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9990000128746033,"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":0.9980999827384949,"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/personalization","display_name":"Personalization","score":0.8813128471374512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7593969106674194},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.7270816564559937},{"id":"https://openalex.org/keywords/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.6843690872192383},{"id":"https://openalex.org/keywords/arousal","display_name":"Arousal","score":0.6122465133666992},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5908893942832947},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5890645980834961},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5828678607940674},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5203849077224731},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49327796697616577},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.4355085492134094},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06164166331291199}],"concepts":[{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.8813128471374512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7593969106674194},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.7270816564559937},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.6843690872192383},{"id":"https://openalex.org/C36951298","wikidata":"https://www.wikidata.org/wiki/Q379784","display_name":"Arousal","level":2,"score":0.6122465133666992},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5908893942832947},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5890645980834961},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5828678607940674},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5203849077224731},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49327796697616577},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.4355085492134094},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06164166331291199},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/metroxraine58569.2023.10405681","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine58569.2023.10405681","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","raw_type":"proceedings-article"},{"id":"pmh:oai:boa.unimib.it:10281/462798","is_oa":false,"landing_page_url":"https://hdl.handle.net/10281/462798","pdf_url":null,"source":{"id":"https://openalex.org/S4306401259","display_name":"BOA (University of Milano-Bicocca)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66752286","host_organization_name":"University of Milano-Bicocca","host_organization_lineage":["https://openalex.org/I66752286"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W110731996","https://openalex.org/W1206391664","https://openalex.org/W2067583143","https://openalex.org/W2099741732","https://openalex.org/W2132360759","https://openalex.org/W2149628368","https://openalex.org/W2507248126","https://openalex.org/W2783433009","https://openalex.org/W2790404832","https://openalex.org/W2968176343","https://openalex.org/W2974550657","https://openalex.org/W2997540115","https://openalex.org/W3006281198","https://openalex.org/W3009249197","https://openalex.org/W3033169763","https://openalex.org/W3091698088","https://openalex.org/W3109703861","https://openalex.org/W3132593278","https://openalex.org/W3160343815","https://openalex.org/W3207988286","https://openalex.org/W4251591997","https://openalex.org/W4310874932","https://openalex.org/W4312984402","https://openalex.org/W4365128528","https://openalex.org/W4367854610","https://openalex.org/W4400134761","https://openalex.org/W6627949721","https://openalex.org/W6768225298"],"related_works":["https://openalex.org/W2029072726","https://openalex.org/W91913183","https://openalex.org/W2936882366","https://openalex.org/W2736893848","https://openalex.org/W2128698257","https://openalex.org/W1544055438","https://openalex.org/W3003450285","https://openalex.org/W2013608943","https://openalex.org/W4399628019","https://openalex.org/W2085024878"],"abstract_inverted_index":{"Personalization":[0],"is":[1],"essential":[2],"in":[3,11,19,56,113],"enhancing":[4],"the":[5,29,49,95,99,102,122,126],"performance":[6,100],"of":[7,31,51,101],"machine":[8,53],"learning":[9,54,80],"models":[10,34,103,124,128],"brain-computer":[12],"interfaces":[13],"(BCIs)":[14],"for":[15],"emotion":[16],"recognition,":[17],"specifically":[18],"valence":[20,58,114],"and":[21,59,68,105,115,133],"arousal":[22,116],"classification.":[23],"In":[24],"this":[25],"work,":[26],"we":[27,76],"address":[28],"challenge":[30],"personalizing":[32],"BCI":[33],"utilizing":[35],"a":[36],"wireless":[37],"consumer":[38],"non-invasive":[39],"electroencephalogram":[40],"(EEG)":[41],"device":[42],"with":[43,121],"dry":[44],"electrodes.":[45],"Our":[46],"research":[47],"investigates":[48],"effectiveness":[50],"three":[52],"algorithms":[55],"classifying":[57],"arousal:":[60],"k-Nearest":[61],"Neighbors":[62],"(k-NNs),":[63],"Support":[64],"Vector":[65],"Machines":[66],"(SVMs),":[67],"Artificial":[69],"Neural":[70],"Networks":[71],"(ANNs).":[72],"To":[73],"achieve":[74],"personalization,":[75,120],"adopt":[77],"an":[78],"incremental":[79],"approach":[81],"by":[82,129],"progressively":[83],"incorporating":[84],"high-quality":[85],"subject":[86],"data":[87],"during":[88],"model":[89],"training":[90],"that":[91],"are":[92],"taken":[93],"from":[94],"ground-truth.":[96],"We":[97],"compare":[98],"before":[104],"after":[106],"personalization.":[107],"The":[108],"results":[109],"demonstrate":[110],"significant":[111],"improvements":[112],"classification":[117],"accuracy":[118],"through":[119],"personalized":[123],"outperforming":[125],"non-personalized":[127],"up":[130],"to":[131],"27.82%":[132],"28.80%,":[134],"respectively.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}