{"id":"https://openalex.org/W7137956881","doi":"https://doi.org/10.48550/arxiv.2603.13272","title":"CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models","display_name":"CAMEL-CLIP: Channel-aware Multimodal Electroencephalography-text Alignment for Generalizable Brain Foundation Models","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7137956881","doi":"https://doi.org/10.48550/arxiv.2603.13272"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13272","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13272","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13272","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011420349","display_name":"Hanseul Choi","orcid":"https://orcid.org/0000-0002-1209-4679"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Choi, Hanseul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129692137","display_name":"Jinyeong Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Jinyeong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079755989","display_name":"Seongwon Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Seongwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125683490","display_name":"Sungho Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Sungho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125705707","display_name":"Jibum Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jibum","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5011420349"],"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8925999999046326,"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":0.8925999999046326,"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/T10094","display_name":"Epilepsy research and treatment","score":0.019200000911951065,"subfield":{"id":"https://openalex.org/subfields/2738","display_name":"Psychiatry and Mental health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","score":0.01889999955892563,"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/channel","display_name":"Channel (broadcasting)","score":0.6923999786376953},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.67330002784729},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5940999984741211},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4027999937534332},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.3716999888420105},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3483000099658966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.744700014591217},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6923999786376953},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.67330002784729},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5940999984741211},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.578000009059906},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4027999937534332},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3910999894142151},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3483000099658966},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3434000015258789},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3407999873161316},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25769999623298645}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13272","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13272","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13272","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13272","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6049916744232178}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Electroencephalography":[0],"(EEG)":[1],"foundation":[2,40,123],"models":[3,124],"have":[4],"shown":[5],"promise":[6],"for":[7],"learning":[8,101],"generalizable":[9],"representations,":[10],"yet":[11],"they":[12],"remain":[13],"sensitive":[14],"to":[15,43,46,53,102],"channel":[16,22,48,63,75,85],"heterogeneity,":[17],"such":[18],"as":[19],"changes":[20],"in":[21],"composition":[23],"or":[24],"ordering.":[25],"We":[26],"propose":[27],"channel-aware":[28],"multimodal":[29,39],"EEG-text":[30,38],"alignment":[31],"contrastive":[32,37,92,100],"language-image":[33],"pretraining":[34],"(CAMEL-CLIP),":[35],"a":[36],"model":[41],"designed":[42],"be":[44],"robust":[45],"heterogeneous":[47],"configurations":[49],"and":[50,89,98,106,120],"widely":[51],"applicable":[52],"diverse":[54],"downstream":[55],"tasks.":[56],"CAMEL-CLIP":[57,114],"introduces":[58],"three":[59],"key":[60],"components:":[61],"(1)":[62],"attribute-based":[64],"positional":[65],"encoding,":[66],"which":[67,77,94],"identifies":[68],"channels":[69],"through":[70],"semantic":[71],"information;":[72],"(2)":[73],"dynamic":[74],"projection,":[76],"generates":[78],"variable-length":[79],"embeddings":[80],"by":[81],"independently":[82],"projecting":[83],"each":[84],"without":[86],"feature":[87],"compression;":[88],"(3)":[90],"dual-level":[91],"learning,":[93],"jointly":[95],"performs":[96],"channel-level":[97],"sample-level":[99],"capture":[103],"both":[104],"channel-specific":[105],"global":[107],"signal":[108],"characteristics.":[109],"Experimental":[110],"results":[111],"demonstrate":[112],"that":[113,125],"achieves":[115],"state-of-the-art":[116],"performance":[117],"under":[118],"linear-probing":[119],"outperforms":[121],"existing":[122],"rely":[126],"on":[127],"full-finetuning.":[128]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-03-18T00:00:00"}