{"id":"https://openalex.org/W7161744965","doi":"https://doi.org/10.48550/arxiv.2605.16739","title":"EmoMind: Decoding Affective Captions from Human Brain fMRI","display_name":"EmoMind: Decoding Affective Captions from Human Brain fMRI","publication_year":2026,"publication_date":"2026-05-16","ids":{"openalex":"https://openalex.org/W7161744965","doi":"https://doi.org/10.48550/arxiv.2605.16739"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16739","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16739","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.16739","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136483834","display_name":"Bilal A. Mohammed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammed, Bilal A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136463536","display_name":"Lin Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136456424","display_name":"Ruogo Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Ruogu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.30970001220703125,"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":0.30970001220703125,"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/T11094","display_name":"Face Recognition and Perception","score":0.2818000018596649,"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/T12650","display_name":"Aesthetic Perception and Analysis","score":0.11860000342130661,"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/robustness","display_name":"Robustness (evolution)","score":0.6340000033378601},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.628600001335144},{"id":"https://openalex.org/keywords/brain-activity-and-meditation","display_name":"Brain activity and meditation","score":0.4422000050544739},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.41290000081062317},{"id":"https://openalex.org/keywords/neuroimaging","display_name":"Neuroimaging","score":0.3912000060081482},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.37779998779296875},{"id":"https://openalex.org/keywords/human-brain","display_name":"Human brain","score":0.3449000120162964},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3407999873161316}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6340000033378601},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.628600001335144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6010000109672546},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4975000023841858},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.46619999408721924},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.4424000084400177},{"id":"https://openalex.org/C120843803","wikidata":"https://www.wikidata.org/wiki/Q4955807","display_name":"Brain activity and meditation","level":3,"score":0.4422000050544739},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C58693492","wikidata":"https://www.wikidata.org/wiki/Q551875","display_name":"Neuroimaging","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35839998722076416},{"id":"https://openalex.org/C2777670902","wikidata":"https://www.wikidata.org/wiki/Q492038","display_name":"Human brain","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3305000066757202},{"id":"https://openalex.org/C2781441883","wikidata":"https://www.wikidata.org/wiki/Q1632835","display_name":"Precuneus","level":3,"score":0.32589998841285706},{"id":"https://openalex.org/C2776035688","wikidata":"https://www.wikidata.org/wiki/Q1606558","display_name":"Affect (linguistics)","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C2776141551","wikidata":"https://www.wikidata.org/wiki/Q16000087","display_name":"Emotion perception","level":3,"score":0.2903999984264374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2793999910354614},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.2784000039100647},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16739","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16739","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.16739","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16739","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.40265515446662903,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Decoding":[0],"visual":[1,69],"experience":[2],"from":[3,55,67,81],"brain":[4,224],"activity":[5],"has":[6],"advanced":[7],"substantially,":[8],"but":[9,32],"current":[10],"brain-to-text":[11],"systems":[12],"largely":[13],"recover":[14],"semantic":[15,112],"content":[16,91],"while":[17],"discarding":[18],"affect.":[19],"Additionally,":[20],"language":[21],"models":[22],"can":[23],"generate":[24],"emotional":[25],"text":[26],"when":[27],"prompted":[28,158],"with":[29,100,122,139,159,184],"categorical":[30],"labels,":[31],"such":[33],"labels":[34,163],"collapse":[35],"rich":[36],"inter-subject":[37],"variability":[38],"into":[39],"coarse":[40],"discrete":[41,167],"bins.":[42],"We":[43,117,134],"present":[44],"EmoMind,":[45],"the":[46,82,88,98,148,185],"first":[47,59],"end-to-end":[48],"pipeline":[49],"for":[50,211,220],"decoding":[51],"affective":[52,94,115,119,193,213,223],"captions":[53],"directly":[54],"fMRI":[56,84,173],"signals.":[57],"EmoMind":[58,175],"retrieves":[60],"a":[61,75,123,140,165,207],"semantically":[62],"grounded":[63],"neutral":[64],"scene":[65],"description":[66],"brain-decoded":[68,160,204],"features,":[70],"then":[71],"rewrites":[72],"it":[73],"using":[74],"continuous":[76,203],"34-dimensional":[77],"emotion":[78,162,172,198],"vector":[79],"decoded":[80],"same":[83],"recording.":[85],"To":[86],"control":[87,209],"balance":[89],"between":[90,111],"preservation":[92],"and":[93,114,131,151,216],"expression,":[95],"we":[96,152],"train":[97],"rewriter":[99],"classifier-free":[101],"guidance":[102],"against":[103,156],"an":[104],"identity-preserving":[105],"null":[106],"branch,":[107],"enabling":[108],"smooth":[109],"interpolation":[110],"fidelity":[113],"expressivity.":[116],"evaluate":[118],"caption":[120,214],"generation":[121,215],"three-axis":[124],"validation":[125],"framework":[126,138],"spanning":[127],"subject-specificity,":[128],"structural":[129],"geometry,":[130],"causal":[132],"control.":[133],"further":[135],"augment":[136],"this":[137],"synthetic-brain":[141],"substitution":[142],"test":[143],"that":[144,190],"probes":[145],"robustness":[146],"to":[147],"measurement":[149],"apparatus,":[150],"benchmark":[153],"each":[154],"axis":[155],"GPT-4":[157,179],"top-5":[161],"as":[164,206],"strong":[166],"baseline.":[168],"Across":[169],"two":[170],"independent":[171],"datasets,":[174],"significantly":[176],"outperforms":[177],"label-prompted":[178],"on":[180,188],"all":[181],"three":[182],"axes,":[183],"largest":[186],"gains":[187],"metrics":[189],"require":[191],"person-specific":[192],"structure":[194],"rather":[195],"than":[196],"population-level":[197],"aggregation.":[199],"These":[200],"results":[201],"establish":[202],"affect":[205],"viable":[208],"signal":[210],"individualized":[212],"open":[217],"new":[218],"directions":[219],"studying":[221],"individual":[222],"organisation.":[225]},"counts_by_year":[],"updated_date":"2026-06-16T07:32:37.131356","created_date":"2026-05-20T00:00:00"}