{"id":"https://openalex.org/W4408899129","doi":"https://doi.org/10.1109/bci65088.2025.10931275","title":"fNIRS Foundation Model for Few-Shot Based fNIRS Classification","display_name":"fNIRS Foundation Model for Few-Shot Based fNIRS Classification","publication_year":2025,"publication_date":"2025-02-24","ids":{"openalex":"https://openalex.org/W4408899129","doi":"https://doi.org/10.1109/bci65088.2025.10931275"},"language":"en","primary_location":{"id":"doi:10.1109/bci65088.2025.10931275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci65088.2025.10931275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 13th International Conference on Brain-Computer Interface (BCI)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045525623","display_name":"Euijin Jung","orcid":"https://orcid.org/0000-0003-4076-5342"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Euijin Jung","raw_affiliation_strings":["DGIST,Division of Intelligent Robot,Daegu,Korea, Rep"],"affiliations":[{"raw_affiliation_string":"DGIST,Division of Intelligent Robot,Daegu,Korea, Rep","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101736673","display_name":"Hyunmin Lee","orcid":"https://orcid.org/0000-0002-7970-2050"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunmin Lee","raw_affiliation_strings":["School of Interdisciplinary Studies, DGIST,Daegu,Korea, Rep"],"affiliations":[{"raw_affiliation_string":"School of Interdisciplinary Studies, DGIST,Daegu,Korea, Rep","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112030151","display_name":"Jinung An","orcid":null},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinung An","raw_affiliation_strings":["School of Interdisciplinary Studies, DGIST,Division of Intelligent Robot,Daegu,Korea, Rep"],"affiliations":[{"raw_affiliation_string":"School of Interdisciplinary Studies, DGIST,Division of Intelligent Robot,Daegu,Korea, Rep","institution_ids":["https://openalex.org/I193352282"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045525623"],"corresponding_institution_ids":["https://openalex.org/I193352282"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09585857,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9257000088691711,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9257000088691711,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6716615557670593},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.5774327516555786},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.5747297406196594},{"id":"https://openalex.org/keywords/shot","display_name":"Shot (pellet)","score":0.520592212677002},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.495837539434433},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34488222002983093},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.15590786933898926}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6716615557670593},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.5774327516555786},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.5747297406196594},{"id":"https://openalex.org/C2778344882","wikidata":"https://www.wikidata.org/wiki/Q278938","display_name":"Shot (pellet)","level":2,"score":0.520592212677002},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.495837539434433},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34488222002983093},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.15590786933898926},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bci65088.2025.10931275","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci65088.2025.10931275","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 13th International Conference on Brain-Computer Interface (BCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2625950646","https://openalex.org/W2751968869","https://openalex.org/W2887400094","https://openalex.org/W2994384968","https://openalex.org/W3005328682","https://openalex.org/W3038218033","https://openalex.org/W3162541751","https://openalex.org/W3200300741","https://openalex.org/W3211983881","https://openalex.org/W4205386513"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W2497720472","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4292659306","https://openalex.org/W4364306694"],"abstract_inverted_index":{"Functional":[0],"near-infrared":[1],"spectroscopy":[2],"(fNIRS)":[3],"is":[4],"a":[5,37,55,64,132],"non-invasive":[6],"technique":[7],"with":[8,137],"significant":[9],"potential":[10],"for":[11,32,59,88,168],"applications":[12],"in":[13,120],"brain-computer":[14],"interfaces":[15],"(BCIs)":[16],"including":[17],"mental":[18],"health":[19],"diagnostics":[20],"and":[21,43,124,151,165],"cognitive":[22],"state":[23],"monitoring.":[24],"However,":[25],"the":[26,41,79,95,110,138,145,159],"reliance":[27],"on":[28,63,75,81],"large":[29],"labeled":[30,82,111],"datasets":[31,83],"high-performing":[33],"classification":[34,154,170],"methods":[35],"poses":[36],"critical":[38],"challenge,":[39],"given":[40],"time-consuming":[42],"resource-intensive":[44],"nature":[45,140],"of":[46,109,141],"fNIRS":[47,60,142],"data":[48,61],"collection.":[49],"To":[50],"address":[51],"this,":[52],"we":[53],"propose":[54],"novel":[56],"foundation":[57],"model":[58,97,146],"based":[62],"self-supervised":[65,118],"masked":[66],"autoencoder":[67],"framework.":[68],"The":[69],"proposed":[70,96,160],"method":[71,161],"enables":[72],"efficient":[73],"pre-training":[74],"unlabeled":[76],"data,":[77],"reducing":[78],"dependence":[80],"while":[84,105],"maintaining":[85],"robust":[86],"performance":[87,99],"downstream":[89],"tasks.":[90,171],"Experimental":[91],"results":[92],"demonstrate":[93],"that":[94,131],"achieves":[98],"comparable":[100],"to":[101,147],"supervised":[102],"learning":[103],"approaches":[104],"requiring":[106],"only":[107],"one-third":[108],"training":[112],"data.":[113],"It":[114],"consistently":[115],"outperforms":[116],"state-of-the-art":[117],"models":[119],"both":[121],"linear":[122],"probing":[123],"fine-tuning":[125],"settings.":[126],"Moreover,":[127],"ablation":[128],"studies":[129],"show":[130],"larger":[133],"masking":[134],"size":[135],"aligns":[136],"low-frequency":[139],"signals,":[143],"enabling":[144],"capture":[148],"broader":[149],"patterns":[150],"further":[152],"enhance":[153],"accuracy.":[155],"These":[156],"findings":[157],"validate":[158],"as":[162],"an":[163],"effective":[164],"scalable":[166],"solution":[167],"fNIRS-based":[169]},"counts_by_year":[],"updated_date":"2025-12-19T19:40:27.379048","created_date":"2025-10-10T00:00:00"}
