{"id":"https://openalex.org/W7138009847","doi":"https://doi.org/10.1088/1741-2552/aeac87","title":"The challenge of out-of-distribution detection in motor imagery BCIs","display_name":"The challenge of out-of-distribution detection in motor imagery BCIs","publication_year":2026,"publication_date":"2026-09-25","ids":{"openalex":"https://openalex.org/W7138009847","doi":"https://doi.org/10.1088/1741-2552/aeac87","pmid":"https://pubmed.ncbi.nlm.nih.gov/42790484"},"language":"en","primary_location":{"id":"doi:10.1088/1741-2552/aeac87","doi":"https://doi.org/10.1088/1741-2552/aeac87","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1741-2552/aeac87","pdf_url":"https://iopscience.iop.org/article/10.1088/1741-2552/aeac87/pdf","source":{"id":"https://openalex.org/S32898093","display_name":"Journal of Neural Engineering","issn_l":"1741-2552","issn":["1741-2552","1741-2560"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neural Engineering","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://iopscience.iop.org/article/10.1088/1741-2552/aeac87/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129722519","display_name":"Merlijn Quincent Mulder","orcid":"https://orcid.org/0009-0002-0880-1059","observed_orcids":["https://orcid.org/0009-0002-0880-1059"]},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Merlijn Quincent Mulder","raw_affiliation_strings":["University of Groningen"],"raw_orcid":"https://orcid.org/0009-0002-0880-1059","affiliations":[{"raw_affiliation_string":"University of Groningen","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013519837","display_name":"Mat\u00edas Valdenegro-Toro","orcid":"https://orcid.org/0000-0001-5793-9498","observed_orcids":["https://orcid.org/0000-0001-5793-9498"]},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Matias Valdenegro-Toro","raw_affiliation_strings":["University of Groningen"],"raw_orcid":"https://orcid.org/0000-0001-5793-9498","affiliations":[{"raw_affiliation_string":"University of Groningen","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005668305","display_name":"Andreea Ioana Sburlea","orcid":"https://orcid.org/0000-0001-6766-3464","observed_orcids":["https://orcid.org/0000-0001-6766-3464"]},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Andreea Ioana Sburlea","raw_affiliation_strings":["University of Groningen"],"raw_orcid":"https://orcid.org/0000-0001-6766-3464","affiliations":[{"raw_affiliation_string":"University of Groningen","institution_ids":["https://openalex.org/I169381384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5093524871","display_name":"Ivo Pascal de Jong","orcid":"https://orcid.org/0000-0002-1497-8013","observed_orcids":["https://orcid.org/0000-0002-1497-8013"]},"institutions":[{"id":"https://openalex.org/I169381384","display_name":"University of Groningen","ror":"https://ror.org/012p63287","country_code":"NL","type":"education","lineage":["https://openalex.org/I169381384"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Ivo Pascal de Jong","raw_affiliation_strings":["University of Groningen"],"raw_orcid":"https://orcid.org/0000-0002-1497-8013","affiliations":[{"raw_affiliation_string":"University of Groningen","institution_ids":["https://openalex.org/I169381384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5093524871"],"corresponding_institution_ids":["https://openalex.org/I169381384"],"apc_list":{"value":3325,"currency":"USD","value_usd":3325},"apc_paid":{"value":3325,"currency":"USD","value_usd":3325},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16951586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"23","issue":"5","first_page":"056046","last_page":"056046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9951000213623047,"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":0.9951000213623047,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10784","display_name":"Muscle activation and electromyography studies","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/out-of-distribution-detection","display_name":"out-of-distribution detection","score":1.0},{"id":"https://openalex.org/keywords/brain-computer-interface","display_name":"brain-computer interface","score":0.8679999709129333},{"id":"https://openalex.org/keywords/motor-imagery","display_name":"motor imagery","score":0.9919999837875366},{"id":"https://openalex.org/keywords/eeg-classification","display_name":"EEG classification","score":0.9399999976158142},{"id":"https://openalex.org/keywords/uncertainty-estimation","display_name":"uncertainty estimation","score":0.9100000262260437},{"id":"https://openalex.org/keywords/monte-carlo-dropout","display_name":"Monte Carlo dropout","score":0.8840000033378601},{"id":"https://openalex.org/keywords/classifier-robustness","display_name":"classifier robustness","score":0.6759999990463257}],"concepts":[{"id":"https://openalex.org/C54808283","wikidata":"https://www.wikidata.org/wiki/Q6918191","display_name":"Motor imagery","level":4,"score":0.6850000023841858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6834999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6571999788284302},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5871999859809875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5331000089645386},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.49309998750686646},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.3797000050544739},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3684000074863434},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3384000062942505},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.250900000333786}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007092","descriptor_name":"Imagination","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":true},{"descriptor_ui":"D009068","descriptor_name":"Movement","qualifier_ui":"Q000502","qualifier_name":"physiology","is_major_topic":false},{"descriptor_ui":"D062207","descriptor_name":"Brain-Computer Interfaces","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1088/1741-2552/aeac87","doi":"https://doi.org/10.1088/1741-2552/aeac87","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1741-2552/aeac87","pdf_url":"https://iopscience.iop.org/article/10.1088/1741-2552/aeac87/pdf","source":{"id":"https://openalex.org/S32898093","display_name":"Journal of Neural Engineering","issn_l":"1741-2552","issn":["1741-2552","1741-2560"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neural Engineering","raw_type":"journal-article"},{"id":"pmid:42790484","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42790484","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of neural engineering","raw_type":"Journal Article"},{"id":"pmh:oai:arXiv.org:2603.13324","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2603.13324","pdf_url":"https://arxiv.org/pdf/2603.13324","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:pure.rug.nl:openaire_cris_publications/00ba06db-81d9-4697-86c3-71b13574eb0f","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/11370/00ba06db-81d9-4697-86c3-71b13574eb0f","pdf_url":null,"source":{"id":"https://openalex.org/S4306400420","display_name":"University of Groningen research database (University of Groningen / Centre for Information Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I169381384","host_organization_name":"University of Groningen","host_organization_lineage":["https://openalex.org/I169381384"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Mulder, M, Valdenegro-Toro, M, Sburlea, A I & de Jong, I P 2026 'The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs' arXiv. https://doi.org/10.48550/arXiv.2603.13324","raw_type":"info:eu-repo/semantics/preprint"},{"id":"doi:10.48550/arxiv.2603.13324","doi":"https://doi.org/10.48550/arxiv.2603.13324","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13324","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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:10.1088/1741-2552/aeac87","doi":"https://doi.org/10.1088/1741-2552/aeac87","is_oa":true,"landing_page_url":"https://doi.org/10.1088/1741-2552/aeac87","pdf_url":"https://iopscience.iop.org/article/10.1088/1741-2552/aeac87/pdf","source":{"id":"https://openalex.org/S32898093","display_name":"Journal of Neural Engineering","issn_l":"1741-2552","issn":["1741-2552","1741-2560"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","dongbi-b","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320083","host_organization_name":"IOP Publishing","host_organization_lineage":["https://openalex.org/P4310320083","https://openalex.org/P4310311669"],"host_organization_lineage_names":["IOP Publishing","Institute of Physics"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Neural Engineering","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.4043000042438507}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138009847.pdf","grobid_xml":"https://content.openalex.org/works/W7138009847.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Objective":[1],".":[2,63,117,238],"Machine":[3],"learning":[4,163],"classifiers":[5],"used":[6],"in":[7,68,145,160,175,266,275],"Brain-Computer":[8],"Interfaces":[9],"(BCIs)":[10],"make":[11,35],"classifications":[12],"based":[13,89],"on":[14,19,76,90],"the":[15,54,110,122,125,135,140,206,267,272,292],"distribution":[16],"of":[17,39,56,112,283,297],"examples":[18],"which":[20],"they":[21,32],"were":[22,199,263],"trained.":[23],"When":[24],"exposed":[25],"to":[26,52,108,139,166,201,217,290],"electroencephalography":[27],"(EEG)":[28],"from":[29],"unfamiliar":[30,59,83,171,232,258],"classes":[31,78,128],"can":[33,86,129],"only":[34],"blind":[36],"guesses.":[37],"Instead":[38],"allowing":[40],"such":[41],"guesses,":[42],"these":[43],"out-of-distribution":[44],"(OOD)":[45],"samples":[46],"should":[47],"be":[48,87,131,167],"detected":[49,88],"and":[50,79,100,190,195,212,224,295],"rejected":[51],"improve":[53,291],"robustness":[55],"BCIs":[57,71,298],"against":[58,257],"cognitive":[60,233],"states.":[61],"Approach":[62],"We":[64,93],"study":[65],"OOD":[66,97,113,136,152,182,213,244,284],"detection":[67,98,153,183,214,245],"Motor":[69,276],"Imagery":[70,277],"by":[72,299],"training":[73,268],"a":[74,149,242],"model":[75],"some":[77],"observing":[80],"whether":[81,253],"an":[82,248],"movement":[84],"class":[85],"increased":[91],"uncertainty.":[92],"tested":[94],"seven":[95],"different":[96],"methods":[99,154],"one":[101],"more":[102],"method":[103],"that":[104,155,181,191,221,262,279],"has":[105],"been":[106],"claimed":[107],"boost":[109],"quality":[111],"detection.":[114],"Main":[115],"results":[116,287],"For":[118],"many":[119,151],"BCI":[120,278],"users,":[121],"uncertainty":[123],"for":[124,134,222,251],"familiar":[126],"in-distribution":[127],"still":[130],"higher":[132],"than":[133],"classes,":[137],"due":[138],"high":[141],"intrinsic":[142],"variability":[143],"inherent":[144],"EEG":[146],"signals.":[147],"As":[148],"result,":[150],"have":[156],"shown":[157],"good":[158],"performance":[159,184,228],"other":[161],"machine":[162],"domains":[164],"prove":[165],"ineffective":[168],"at":[169],"identifying":[170],"motor":[172,259],"imagery":[173,260],"patterns":[174],"BCIs.":[176],"However,":[177],"we":[178],"also":[179],"found":[180],"is":[185,229,271],"correlated":[186],"with":[187],"on-task":[188,203],"performance,":[189],"Deep":[192],"Ensemble":[193],"models":[194,198,223,254],"Monte":[196],"Carlo-Dropout":[197],"able":[200],"achieve":[202],"Area":[204],"Under":[205],"Receiver-Operator":[207],"Characteristic":[208],"curve":[209],">":[210],"0.9,":[211],"ability":[215],"up":[216],"0.7.":[218],"This":[219,270],"shows":[220],"subjects":[225],"where":[226],"task":[227],"high,":[230],"rejecting":[231],"states":[234],"becomes":[235],"feasible.":[236],"Significance":[237],"Our":[239,286],"research":[240],"demonstrates":[241],"Leave-One-Class-Out":[243],"setup":[246],"as":[247],"experimental":[249],"paradigm":[250],"studying":[252],"are":[255],"robust":[256],"actions":[261],"not":[264],"seen":[265],"data.":[269,285],"first":[273],"benchmark":[274],"evaluates":[280],"this":[281],"type":[282],"demonstrate":[288],"how":[289],"overall":[293],"safety":[294],"reliability":[296],"preventing":[300],"erroneous":[301],"actions.":[302]},"counts_by_year":[],"updated_date":"2026-10-11T09:32:39.932853","created_date":"2026-03-18T00:00:00"}
