{"id":"https://openalex.org/W7161742847","doi":"https://doi.org/10.1109/access.2026.3694984","title":"EmoRecog-Net: A Multimodal Framework for Emotion Recognition From fMRI and Physiological Signals","display_name":"EmoRecog-Net: A Multimodal Framework for Emotion Recognition From fMRI and Physiological Signals","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7161742847","doi":"https://doi.org/10.1109/access.2026.3694984"},"language":"en","primary_location":{"id":"doi:10.1109/access.2026.3694984","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3694984","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3694984","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044674800","display_name":"Hsiang-Chin Chien","orcid":"https://orcid.org/0000-0002-5150-7958"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Hsiang-Chin Chien","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5150-7958","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021866378","display_name":"Chia\u2010Yen Lee","orcid":"https://orcid.org/0000-0002-2928-3337"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Yen Lee","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-7477-782X","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055106247","display_name":"Jung-Chih Chen","orcid":"https://orcid.org/0000-0003-3958-6806"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jung-Chih Chen","raw_affiliation_strings":["Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-3958-6806","affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I148366613"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73904067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"77401","last_page":"77417"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.9708999991416931,"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":0.9708999991416931,"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":0.005499999970197678,"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/T11094","display_name":"Face Recognition and Perception","score":0.0012000000569969416,"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/emotion-recognition","display_name":"Emotion recognition","score":0.6141999959945679},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3450999855995178},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.334199994802475},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2906999886035919},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.27390000224113464},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.2685999870300293}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7263000011444092},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.6141999959945679},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45329999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40630000829696655},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.2685999870300293},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2614000141620636},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2590000033378601},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.25850000977516174},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.25450000166893005},{"id":"https://openalex.org/C206310091","wikidata":"https://www.wikidata.org/wiki/Q750859","display_name":"Emotion classification","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2026.3694984","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3694984","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9bfefbcd982e45e595be23f5da29c55b","is_oa":true,"landing_page_url":"https://doaj.org/article/9bfefbcd982e45e595be23f5da29c55b","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":"IEEE Access, Vol 14, Pp 77401-77417 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3694984","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3694984","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Emotion":[0],"emerges":[1],"from":[2],"the":[3,100,123,128,136,142,146],"coordinated":[4],"interplay":[5],"between":[6],"central":[7],"neural":[8,74],"activity":[9],"and":[10,32,38,58,66,87,111,127,153,165,174,189],"peripheral":[11],"autonomic":[12],"responses;":[13],"however,":[14],"many":[15],"existing":[16],"deep":[17],"learning":[18],"approaches":[19],"model":[20,84],"these":[21],"modalities":[22],"independently,":[23],"limiting":[24],"their":[25],"ability":[26],"to":[27,33,76,83,91],"capture":[28],"complementary":[29],"affective":[30,194],"information":[31],"remain":[34],"robust":[35,188],"under":[36,122,172],"small-dataset":[37],"partially":[39],"missing-data":[40],"conditions.":[41,176],"To":[42],"address":[43],"this":[44,46],"issue,":[45],"study":[47],"presents":[48],"EmoRecog-Net,":[49],"a":[50,88,187,199],"unified":[51],"multimodal":[52,103,167,193,209],"framework":[53],"for":[54,192,204],"video-induced":[55],"emotion":[56,205],"categorization":[57],"recognition":[59],"using":[60,119],"functional":[61],"magnetic":[62],"resonance":[63],"imaging":[64],"(fMRI)":[65],"physiological":[67],"signals.":[68,114],"The":[69,132],"proposed":[70,133],"architecture":[71],"integrates":[72],"convolutional":[73],"networks":[75],"learn":[77],"spatial":[78],"representations,":[79],"gated":[80],"recurrent":[81],"units":[82],"temporal":[85,163],"dynamics,":[86],"multilayer":[89],"perceptron":[90],"perform":[92],"nonlinear":[93],"cross-modal":[94],"fusion.":[95],"Experiments":[96],"were":[97],"conducted":[98],"on":[99,145],"ICBHI":[101],"2024":[102],"benchmark,":[104,147],"which":[105],"includes":[106],"synchronized":[107],"fMRI,":[108],"photoplethysmography":[109],"(PPG),":[110],"respiration":[112],"(R)":[113],"Model":[115],"performance":[116,171],"was":[117],"evaluated":[118],"F1-score,":[120],"area":[121],"curve":[124],"(AUC),":[125],"accuracy,":[126],"official":[129],"Finalscore":[130,138],"metric.":[131],"method":[134],"achieved":[135],"lowest":[137],"of":[139],"0.2815":[140],"among":[141],"compared":[143],"methods":[144],"outperforming":[148],"representative":[149],"CNN-LSTM,":[150],"attention-based,":[151],"graph-based,":[152],"autoencoder-based":[154],"baselines.":[155],"In":[156],"addition,":[157],"ablation":[158],"studies":[159],"demonstrated":[160],"that":[161,180],"GRU-based":[162],"modeling":[164,182],"MLP-based":[166],"fusion":[168],"consistently":[169],"improved":[170],"limited-data":[173],"class-imbalanced":[175],"These":[177],"results":[178],"indicate":[179],"jointly":[181],"coupled":[183],"brain-autonomic":[184],"dynamics":[185],"provides":[186],"effective":[190,202],"strategy":[191],"computing.":[195],"Overall,":[196],"EmoRecog-Net":[197],"offers":[198],"compact":[200],"yet":[201],"design":[203],"decoding":[206],"in":[207],"small-scale":[208],"settings.":[210]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2026-05-20T00:00:00"}