{"id":"https://openalex.org/W7161714585","doi":"https://doi.org/10.48550/arxiv.2605.18172","title":"Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs","display_name":"Visualizing the Invisible: Generative Visual Grounding Empowers Universal EEG Understanding in MLLMs","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161714585","doi":"https://doi.org/10.48550/arxiv.2605.18172"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18172","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.18172","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136502463","display_name":"Junyu Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Jun-Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136487787","display_name":"Yansen Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yansen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136499463","display_name":"Enze Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Enze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135718308","display_name":"Baoliang L\u00fc","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Bao-Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135645082","display_name":"Weilong Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Wei-Long","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136459426","display_name":"Dongsheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Dongsheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3271999955177307,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.3271999955177307,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14509999752044678,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.07989999651908875,"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/generative-grammar","display_name":"Generative grammar","score":0.5303999781608582},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.4927000105381012},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.474700003862381},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.46480000019073486},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.45969998836517334},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.4586000144481659},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.444599986076355},{"id":"https://openalex.org/keywords/complement","display_name":"Complement (music)","score":0.40149998664855957},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6940000057220459},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5508000254631042},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5303999781608582},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.46480000019073486},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.45969998836517334},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4586000144481659},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C112313634","wikidata":"https://www.wikidata.org/wiki/Q7886648","display_name":"Complement (music)","level":5,"score":0.40149998664855957},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.3824000060558319},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3377000093460083},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32190001010894775},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2780103172","wikidata":"https://www.wikidata.org/wiki/Q1309721","display_name":"Visual Objects","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C120843803","wikidata":"https://www.wikidata.org/wiki/Q4955807","display_name":"Brain activity and meditation","level":3,"score":0.2709999978542328},{"id":"https://openalex.org/C149364088","wikidata":"https://www.wikidata.org/wiki/Q185917","display_name":"Translation (biology)","level":4,"score":0.2606000006198883},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2565000057220459},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.25360000133514404},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18172","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.18172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18172","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.45055094361305237,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Leveraging":[0],"the":[1,57,115],"universal":[2],"representations":[3,151],"of":[4,70],"pre-trained":[5],"LLMs":[6],"and":[7,108,146,162],"MLLMs":[8,90],"offers":[9],"a":[10,34,53,66,128],"promising":[11],"path":[12],"toward":[13],"brain":[14,45],"foundation":[15],"models.":[16],"However,":[17],"visually-evoked":[18],"EEG":[19,72,160],"datasets":[20],"remain":[21],"scarce,":[22],"leading":[23],"existing":[24],"methods":[25],"to":[26,91,173],"align":[27],"neural":[28,150],"signals":[29],"mainly":[30],"with":[31,136,152],"abstract":[32],"text,":[33],"lossy":[35],"translation":[36],"that":[37,55,88],"may":[38],"discard":[39],"fine-grained":[40],"perceptual":[41,153],"information":[42],"encoded":[43],"in":[44,159],"activity.":[46],"We":[47,99,132],"propose":[48],"Generative":[49],"Visual":[50],"Grounding":[51],"(GVG),":[52],"framework":[54],"visualizes":[56],"invisible":[58],"by":[59],"using":[60],"an":[61,170],"EEG-to-image":[62],"generative":[63],"model":[64],"as":[65,169],"visual":[67,86,94,147,163,166],"translator.":[68],"Instead":[69],"forcing":[71],"into":[73],"text":[74,141],"alone,":[75],"GVG":[76],"hallucinates":[77],"instance-specific":[78],"proxy":[79,167],"images":[80],"for":[81,96],"non-visual":[82],"EEG,":[83],"providing":[84],"structured":[85],"contexts":[87],"allow":[89],"exploit":[92],"their":[93],"priors":[95],"clinical-state":[97],"interpretation.":[98],"validate":[100],"this":[101],"idea":[102],"on":[103,127],"two":[104],"MLLM":[105],"backbones,":[106],"GVG-X-Omni":[107,117],"GVG-Janus.":[109],"Image-only":[110],"alignment":[111],"is":[112],"already":[113],"competitive:":[114],"lightweight":[116],"matches":[118],"1.7B-parameter":[119],"text-aligned":[120],"baselines":[121],"while":[122],"tuning":[123],"only":[124],"170M":[125],"parameters":[126],"frozen":[129],"7B":[130],"backbone.":[131],"further":[133],"extend":[134],"GVG-Janus":[135],"trimodal":[137],"Image+Text":[138],"alignment,":[139],"where":[140],"supplies":[142],"categorical":[143],"semantic":[144],"anchors":[145],"proxies":[148],"enrich":[149],"details.":[154],"Experiments":[155],"show":[156],"consistent":[157],"gains":[158],"understanding":[161],"generation,":[164],"suggesting":[165],"grounding":[168],"effective":[171],"complement":[172],"textual":[174],"alignment.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
