{"id":"https://openalex.org/W7165793209","doi":"https://doi.org/10.48550/arxiv.2606.23707","title":"Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation","display_name":"Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7165793209","doi":"https://doi.org/10.48550/arxiv.2606.23707"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23707","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.23707","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139237842","display_name":"Hongjun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Hongjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139235340","display_name":"Leyu Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Leyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139276889","display_name":"Zijianghao Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zijianghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139274952","display_name":"Chao Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Chao","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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.11469999700784683,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.11469999700784683,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.11100000143051147,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular 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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.08950000256299973,"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/electroencephalography","display_name":"Electroencephalography","score":0.7315999865531921},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5665000081062317},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5656999945640564},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5304999947547913},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5023999810218811},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.45649999380111694}],"concepts":[{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7315999865531921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.678600013256073},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5665000081062317},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5656999945640564},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5460000038146973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5304999947547913},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5004000067710876},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.45649999380111694},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4138000011444092},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36309999227523804},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.33649998903274536},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.2994999885559082},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23707","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23707","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":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8080497980117798}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"EEG":[0,77,95,117,165],"spatial":[1],"super-resolution":[2],"(EEGSR)":[3],"in":[4],"real":[5],"deployments":[6],"is":[7],"challenged":[8],"by":[9,21,97,195,200],"random":[10,175],"channel":[11,35,153],"missingness,":[12],"unstable":[13],"electrode":[14,103,112,159,214],"quality,":[15],"and":[16,79,86,119,135,178,197],"changing":[17],"visible-channel":[18],"patterns":[19],"caused":[20],"bad":[22],"contacts":[23],"or":[24],"device":[25],"variability.":[26],"Most":[27],"existing":[28],"EEGSR":[29,57],"methods":[30],"learn":[31],"a":[32,60,71,83,87,138,151],"fixed":[33],"low-to-high":[34],"mapping":[36],"under":[37,157,184],"pre-defined":[38],"input-output":[39],"layouts,":[40],"which":[41],"makes":[42],"them":[43],"brittle":[44],"when":[45],"missing":[46],"channels":[47,78],"vary":[48],"at":[49,101,213],"test":[50],"time.":[51],"In":[52],"this":[53,99],"paper,":[54],"we":[55,148],"reformulate":[56],"as":[58],"learning":[59],"shared":[61],"conditional":[62,88],"scalp":[63,141],"field":[64,142],"from":[65,114],"partially":[66],"observed":[67,76,146],"support":[68,118],"channels.":[69],"Specifically,":[70],"position-guided":[72],"encoder":[73],"summarizes":[74],"the":[75,107,115,120,125,128,133,145,168,185,204],"their":[80],"coordinates":[81],"into":[82],"latent":[84,130],"condition,":[85],"implicit":[89],"neural":[90],"representation":[91,131],"decoder":[92,134],"reconstructs":[93,110],"target":[94],"signals":[96,113,212],"querying":[98],"condition":[100],"desired":[102],"coordinates.":[104,122],"During":[105],"inference,":[106],"model":[108],"directly":[109],"unseen":[111],"available":[116],"queried":[121],"To":[123],"strengthen":[124],"constraint":[126],"of":[127,170],"encoded":[129],"on":[132,189],"thereby":[136],"construct":[137],"more":[139],"stable":[140],"consistent":[143],"with":[144],"channels,":[147],"further":[149],"introduce":[150],"fidelity-preserving":[152],"corruption":[154],"training":[155],"strategy":[156],"mixed":[158],"states.":[160],"Extensive":[161],"experiments":[162],"across":[163],"multiple":[164],"datasets":[166],"demonstrate":[167],"effectiveness":[169],"our":[171,191],"framework":[172],"for":[173],"both":[174],"missing-channel":[176],"reconstruction":[177],"strict":[179,186],"unseen-electrode":[180],"signal":[181],"generation.":[182],"Notably,":[183],"held-out-electrode":[187],"setting":[188],"AAD,":[190],"method":[192],"reduces":[193],"NMSE":[194],"37.5\\%":[196],"improves":[198],"SNR":[199],"2.12":[201],"dB":[202],"over":[203],"strongest":[205],"baseline,":[206],"showing":[207],"its":[208],"ability":[209],"to":[210],"synthesize":[211],"locations":[215],"never":[216],"exposed":[217],"during":[218],"training.":[219]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
