{"id":"https://openalex.org/W7140198178","doi":"https://doi.org/10.48550/arxiv.2603.20258","title":"The Deep-Match Framework for Event-Related Potential Detection in EEG","display_name":"The Deep-Match Framework for Event-Related Potential Detection in EEG","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7140198178","doi":"https://doi.org/10.48550/arxiv.2603.20258"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20258","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,"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.2603.20258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5146896013","display_name":"Marek Zylinski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zylinski, Marek","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130565909","display_name":"Bartosz Tomasz Smigielski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smigielski, Bartosz Tomasz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027382431","display_name":"Gerard Cybulski","orcid":"https://orcid.org/0000-0002-2917-2047"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cybulski, Gerard","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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9934999942779541,"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":0.9934999942779541,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.0008999999845400453,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.7667999863624573},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.7297999858856201},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6219000220298767},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5072000026702881},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.44519999623298645},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4311999976634979},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.39969998598098755},{"id":"https://openalex.org/keywords/wearable-computer","display_name":"Wearable computer","score":0.3944999873638153},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.3587999939918518}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7849000096321106},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7667999863624573},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7297999858856201},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6219000220298767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6161999702453613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5072000026702881},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4311999976634979},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C150594956","wikidata":"https://www.wikidata.org/wiki/Q1334829","display_name":"Wearable computer","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.36399999260902405},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3587999939918518},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.3571000099182129},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32420000433921814},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.3109999895095825},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.2768999934196472},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2759999930858612},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20258","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,"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.2603.20258","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20258","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,"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reliable":[0],"detection":[1,40,49,90,133],"of":[2,186,220,247,250],"event-related":[3],"potentials":[4],"(ERPs)":[5],"at":[6],"the":[7,16,44,73,81,84,93,144,167,189,194,215,218],"single-trial":[8,131,211],"level":[9],"remains":[10],"a":[11,89,105,113,130,160,235],"major":[12],"challenge":[13],"due":[14],"to":[15,67,75,166,174],"low":[17],"signal-to-noise":[18],"ratio":[19],"EEG":[20,52,70,229,240],"recordings.":[21],"In":[22,80],"this":[23],"work,":[24],"we":[25],"investigate":[26],"whether":[27],"incorporating":[28],"prior":[29],"knowledge":[30,223],"about":[31],"ERP":[32,48,98,123,132,212],"templates":[33],"into":[34],"deep":[35,225],"learning":[36,226],"models":[37],"can":[38,205],"improve":[39],"performance.":[41],"We":[42],"employ":[43],"Deep-Match":[45],"framework":[46],"for":[47,97,150,228],"using":[50,122,135],"multi-channel":[51],"signals.":[53],"The":[54,138,177,231],"model":[55,101,107,115,141,196],"is":[56,65,86,95,127],"trained":[57,66],"in":[58,116,209],"two":[59],"stages.":[60],"First,":[61],"an":[62,184],"encoder-decoder":[63],"architecture":[64],"reconstruct":[68],"input":[69,118],"signals,":[71],"enabling":[72],"network":[74,94,169],"learn":[76],"compact":[77],"signal":[78],"representations.":[79],"second":[82],"stage,":[83],"decoder":[85],"replaced":[87],"with":[88,108,146,224],"module,":[91],"and":[92,112,241],"fine-tuned":[96],"identification.":[99],"Two":[100],"variants":[102],"are":[103,120],"evaluated:":[104],"standard":[106,147,168,195],"randomly":[109],"initialized":[110,121],"filters":[111],"Deep-MF":[114,140,158,182],"which":[117],"kernels":[119],"templates.":[124],"Model":[125],"performance":[126,179],"assessed":[128],"on":[129],"task":[134],"leave-one-subject-out":[136],"validation.":[137],"proposed":[139,232],"slightly":[142],"outperforms":[143],"detector":[145],"kernel":[148,203],"initialization":[149,204],"most":[151],"held-out":[152],"subjects.":[153],"Despite":[154],"substantial":[155],"inter-subject":[156],"variability,":[157],"achieves":[159],"higher":[161],"average":[162],"F1-score":[163,185],"(0.37)":[164],"compared":[165],"(0.34),":[170],"indicating":[171],"improved":[172],"robustness":[173],"cross-subject":[175],"differences.":[176],"best":[178],"obtained":[180],"by":[181,193],"reaches":[183],"0.71,":[187],"exceeding":[188],"maximum":[190],"score":[191],"achieved":[192],"(0.59).":[197],"These":[198],"results":[199],"demonstrate":[200],"that":[201],"ERP-informed":[202],"provide":[206],"consistent":[207],"improvements":[208],"subject-independent":[210],"detection.":[213],"Overall,":[214],"findings":[216],"highlight":[217],"potential":[219],"integrating":[221],"domain":[222],"architectures":[227],"analysis.":[230],"approach":[233],"represents":[234],"step":[236],"toward":[237],"practical":[238],"wearable":[239],"passive":[242],"brain-computer":[243],"interface":[244],"systems":[245],"capable":[246],"real-time":[248],"monitoring":[249],"cognitive":[251],"processes.":[252]},"counts_by_year":[],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2026-03-25T00:00:00"}