{"id":"https://openalex.org/W4308332509","doi":"https://doi.org/10.3390/s22218467","title":"M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity","display_name":"M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity","publication_year":2022,"publication_date":"2022-11-03","ids":{"openalex":"https://openalex.org/W4308332509","doi":"https://doi.org/10.3390/s22218467","pmid":"https://pubmed.ncbi.nlm.nih.gov/36366164"},"language":"en","primary_location":{"id":"doi:10.3390/s22218467","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22218467","pdf_url":"https://www.mdpi.com/1424-8220/22/21/8467/pdf?version=1667473945","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/22/21/8467/pdf?version=1667473945","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044032604","display_name":"Sumya Akter","orcid":"https://orcid.org/0000-0001-7114-1845"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sumya Akter","raw_affiliation_strings":["Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA"],"raw_orcid":"https://orcid.org/0000-0001-7114-1845","affiliations":[{"raw_affiliation_string":"Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076451838","display_name":"Rumman Ahmed Prodhan","orcid":"https://orcid.org/0000-0002-6865-185X"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rumman Ahmed Prodhan","raw_affiliation_strings":["Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA"],"raw_orcid":"https://orcid.org/0000-0002-6865-185X","affiliations":[{"raw_affiliation_string":"Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015314974","display_name":"Tanmoy Sarkar Pias","orcid":"https://orcid.org/0000-0002-7325-9844"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tanmoy Sarkar Pias","raw_affiliation_strings":["Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA"],"raw_orcid":"https://orcid.org/0000-0002-7325-9844","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013355721","display_name":"David Eisenberg","orcid":"https://orcid.org/0000-0002-0890-7390"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"David Eisenberg","raw_affiliation_strings":["Department of Information Systems, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA"],"raw_orcid":"https://orcid.org/0000-0002-0890-7390","affiliations":[{"raw_affiliation_string":"Department of Information Systems, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053522689","display_name":"Jorge Fresneda","orcid":"https://orcid.org/0000-0001-9985-8362"},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jorge Fresneda Fernandez","raw_affiliation_strings":["Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA"],"raw_orcid":"https://orcid.org/0000-0001-9985-8362","affiliations":[{"raw_affiliation_string":"Martin Tuchman School of Management, New Jersey Institute of Technology, Newark, NJ 07102, USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5013355721","https://openalex.org/A5015314974"],"corresponding_institution_ids":["https://openalex.org/I118118575","https://openalex.org/I859038795"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2598},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2598},"fwci":2.368,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.89034325,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"22","issue":"21","first_page":"8467","last_page":"8467"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.9998000264167786,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/computer-science","display_name":"Computer science","score":0.7969150543212891},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7769100666046143},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7190300226211548},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.60389643907547},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5625085830688477},{"id":"https://openalex.org/keywords/emotion-classification","display_name":"Emotion classification","score":0.5427846908569336},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5404276847839355},{"id":"https://openalex.org/keywords/valence","display_name":"Valence (chemistry)","score":0.5338087677955627},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5244848132133484},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.4893004596233368},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4585016667842865},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.39408981800079346},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3846794068813324},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.2792319655418396}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7969150543212891},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7769100666046143},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7190300226211548},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.60389643907547},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5625085830688477},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.5427846908569336},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5404276847839355},{"id":"https://openalex.org/C168900304","wikidata":"https://www.wikidata.org/wiki/Q171407","display_name":"Valence (chemistry)","level":2,"score":0.5338087677955627},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5244848132133484},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.4893004596233368},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4585016667842865},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.39408981800079346},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3846794068813324},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2792319655418396},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004644","descriptor_name":"Emotions","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015203","descriptor_name":"Reproducibility of Results","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015203","descriptor_name":"Reproducibility of Results","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015203","descriptor_name":"Reproducibility of Results","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":6,"locations":[{"id":"doi:10.3390/s22218467","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22218467","pdf_url":"https://www.mdpi.com/1424-8220/22/21/8467/pdf?version=1667473945","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:36366164","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36366164","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:6211d684b26647b789a76771e3aa3f27","is_oa":true,"landing_page_url":"https://doaj.org/article/6211d684b26647b789a76771e3aa3f27","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 22, Iss 21, p 8467 (2022)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/22/21/8467/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/s22218467","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors; Volume 22; Issue 21; Pages: 8467","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:9654596","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9654596","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/112565","is_oa":true,"landing_page_url":"http://hdl.handle.net/10919/112565","pdf_url":null,"source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s22218467","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s22218467","pdf_url":"https://www.mdpi.com/1424-8220/22/21/8467/pdf?version=1667473945","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4308332509.pdf","grobid_xml":"https://content.openalex.org/works/W4308332509.grobid-xml"},"referenced_works_count":86,"referenced_works":["https://openalex.org/W2002055708","https://openalex.org/W2165857685","https://openalex.org/W2405145519","https://openalex.org/W2599124244","https://openalex.org/W2604936044","https://openalex.org/W2731964405","https://openalex.org/W2771734292","https://openalex.org/W2800938746","https://openalex.org/W2803881474","https://openalex.org/W2810418809","https://openalex.org/W2889105179","https://openalex.org/W2909533222","https://openalex.org/W2932628637","https://openalex.org/W2936712672","https://openalex.org/W2938736450","https://openalex.org/W2944401411","https://openalex.org/W2947658250","https://openalex.org/W2960600329","https://openalex.org/W2962905870","https://openalex.org/W2963568316","https://openalex.org/W2981004543","https://openalex.org/W2981372722","https://openalex.org/W2982299617","https://openalex.org/W2983840038","https://openalex.org/W2997560618","https://openalex.org/W2998500327","https://openalex.org/W3003207095","https://openalex.org/W3005864656","https://openalex.org/W3009120439","https://openalex.org/W3009814846","https://openalex.org/W3014215018","https://openalex.org/W3014658201","https://openalex.org/W3016167515","https://openalex.org/W3016775848","https://openalex.org/W3020487153","https://openalex.org/W3027581678","https://openalex.org/W3038474676","https://openalex.org/W3047434002","https://openalex.org/W3082894964","https://openalex.org/W3083218890","https://openalex.org/W3084484668","https://openalex.org/W3089148108","https://openalex.org/W3095937415","https://openalex.org/W3100777112","https://openalex.org/W3102822077","https://openalex.org/W3108087271","https://openalex.org/W3108484628","https://openalex.org/W3108564553","https://openalex.org/W3109961563","https://openalex.org/W3110327404","https://openalex.org/W3116615529","https://openalex.org/W3118932394","https://openalex.org/W3119911037","https://openalex.org/W3123409499","https://openalex.org/W3126625480","https://openalex.org/W3128898807","https://openalex.org/W3138409313","https://openalex.org/W3153990350","https://openalex.org/W3155739706","https://openalex.org/W3157999215","https://openalex.org/W3160343815","https://openalex.org/W3188833721","https://openalex.org/W3193300679","https://openalex.org/W3193682252","https://openalex.org/W3195488783","https://openalex.org/W3195508803","https://openalex.org/W3205803445","https://openalex.org/W3207550576","https://openalex.org/W3209683092","https://openalex.org/W3210145349","https://openalex.org/W3216927580","https://openalex.org/W4200101550","https://openalex.org/W4206290592","https://openalex.org/W4210266523","https://openalex.org/W4211211720","https://openalex.org/W4213199906","https://openalex.org/W4214492190","https://openalex.org/W4229021892","https://openalex.org/W4282596231","https://openalex.org/W4284690152","https://openalex.org/W4284959866","https://openalex.org/W4289110083","https://openalex.org/W4291743749","https://openalex.org/W4293009030","https://openalex.org/W4293661173","https://openalex.org/W4293776887"],"related_works":["https://openalex.org/W2922348724","https://openalex.org/W200322357","https://openalex.org/W2130428257","https://openalex.org/W4308951944","https://openalex.org/W2057366091","https://openalex.org/W4312960290","https://openalex.org/W2032664813","https://openalex.org/W2967180365","https://openalex.org/W2129455854","https://openalex.org/W17944974"],"abstract_inverted_index":{"Emotion":[0],"recognition,":[1],"or":[2],"the":[3,111,117,139,153,180,211,224],"ability":[4],"of":[5,123,165,187,204,223],"computers":[6],"to":[7,21,50,69,137,151],"interpret":[8],"people's":[9,23],"emotional":[10],"states,":[11],"is":[12,119],"a":[13],"very":[14],"active":[15],"research":[16],"area":[17],"with":[18,54,100,121,200],"vast":[19],"applications":[20],"improve":[22],"lives.":[24],"However,":[25],"most":[26,58,112],"image-based":[27],"emotion":[28],"recognition":[29],"techniques":[30],"are":[31,47,67,238],"flawed,":[32],"as":[33,64],"humans":[34],"can":[35,195],"intentionally":[36],"hide":[37],"their":[38],"emotions":[39,53],"by":[40],"changing":[41],"facial":[42],"expressions.":[43],"Consequently,":[44],"brain":[45],"signals":[46,66],"being":[48],"used":[49],"detect":[51],"human":[52],"improved":[55],"accuracy,":[56,192],"but":[57],"proposed":[59,212,229],"systems":[60],"demonstrate":[61,178],"poor":[62],"performance":[63],"EEG":[65,114,188,205],"difficult":[68],"classify":[70],"using":[71,220],"standard":[72],"machine":[73],"learning":[74,77],"and":[75,93,127,148,157,167,193],"deep":[76,146],"techniques.":[78],"This":[79],"paper":[80],"proposes":[81],"two":[82,122],"convolutional":[83,143],"neural":[84],"network":[85],"(CNN)":[86],"models":[87,160],"(M1:":[88],"heavily":[89],"parameterized":[90,96],"CNN":[91,97,159],"model":[92,182,214],"M2:":[94],"lightly":[95],"model)":[98],"coupled":[99],"elegant":[101],"feature":[102],"extraction":[103],"methods":[104],"for":[105,129,145,190,207,235,240],"effective":[106],"recognition.":[107],"In":[108],"this":[109],"study,":[110],"popular":[113],"benchmark":[115],"dataset,":[116,226],"DEAP,":[118],"utilized":[120],"its":[124],"labels,":[125],"valence,":[126],"arousal,":[128],"binary":[130],"classification.":[131,209],"We":[132,176],"use":[133],"Fast":[134],"Fourier":[135],"Transformation":[136],"extract":[138],"frequency":[140],"domain":[141],"features,":[142,147],"layers":[144],"complementary":[149],"features":[150],"represent":[152],"dataset.":[154],"The":[155],"M1":[156],"M2":[158,181,213],"achieve":[161,196],"nearly":[162],"perfect":[163],"accuracy":[164,199,217],"99.89%":[166],"99.22%,":[168],"respectively,":[169],"which":[170],"outperform":[171],"every":[172,236],"previous":[173],"state-of-the-art":[174],"model.":[175],"empirically":[177],"that":[179],"requires":[183],"only":[184,201,221],"2":[185],"seconds":[186],"signal":[189],"99.22%":[191],"it":[194],"over":[197],"96%":[198],"125":[202],"milliseconds":[203],"data":[206],"valence":[208,219],"Moreover,":[210],"achieves":[215],"96.8%":[216],"on":[218],"10%":[222],"training":[225],"demonstrating":[227],"our":[228],"system's":[230],"effectiveness.":[231],"Documented":[232],"implementation":[233],"codes":[234],"experiment":[237],"published":[239],"reproducibility.":[241]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":7}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
