{"id":"https://openalex.org/W4310872906","doi":"https://doi.org/10.1109/metroxraine54828.2022.9967496","title":"Embedding neurophysiological signals","display_name":"Embedding neurophysiological signals","publication_year":2022,"publication_date":"2022-10-26","ids":{"openalex":"https://openalex.org/W4310872906","doi":"https://doi.org/10.1109/metroxraine54828.2022.9967496"},"language":"en","primary_location":{"id":"doi:10.1109/metroxraine54828.2022.9967496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine54828.2022.9967496","pdf_url":null,"source":{"id":"https://openalex.org/S4363608343","display_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","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/A5073652288","display_name":"Pierre Guetschel","orcid":"https://orcid.org/0000-0002-8933-7640"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Pierre Guetschel","raw_affiliation_strings":["Donders Institute for Brain, Cognition and Behaviour Radboud University,Nijmegen,Netherlands","Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, Netherlands"],"affiliations":[{"raw_affiliation_string":"Donders Institute for Brain, Cognition and Behaviour Radboud University,Nijmegen,Netherlands","institution_ids":["https://openalex.org/I145872427"]},{"raw_affiliation_string":"Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, Netherlands","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061411152","display_name":"Thoedore Papadopoulo","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en informatique et en automatique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"funder","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Thoedore Papadopoulo","raw_affiliation_strings":["Universit&#x00E9; C&#x00F4;te d&#x2019;Azur,INRIA,Valbonne,France"],"affiliations":[{"raw_affiliation_string":"Universit&#x00E9; C&#x00F4;te d&#x2019;Azur,INRIA,Valbonne,France","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067573169","display_name":"Michael Tangermann","orcid":"https://orcid.org/0000-0001-6729-0290"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Michael Tangermann","raw_affiliation_strings":["Donders Institute for Brain, Cognition and Behaviour Radboud University,Nijmegen,Netherlands","Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, Netherlands"],"affiliations":[{"raw_affiliation_string":"Donders Institute for Brain, Cognition and Behaviour Radboud University,Nijmegen,Netherlands","institution_ids":["https://openalex.org/I145872427"]},{"raw_affiliation_string":"Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, Netherlands","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5073652288"],"corresponding_institution_ids":["https://openalex.org/I145872427"],"apc_list":null,"apc_paid":null,"fwci":0.401,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.50410798,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"32","issue":null,"first_page":"169","last_page":"174"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":1.0,"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":1.0,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9905999898910522,"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":0.9843000173568726,"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/brain\u2013computer-interface","display_name":"Brain\u2013computer interface","score":0.8035598993301392},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7879117727279663},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.5872225165367126},{"id":"https://openalex.org/keywords/neurophysiology","display_name":"Neurophysiology","score":0.5833229422569275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5821690559387207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5671695470809937},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5546452403068542},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.5437227487564087},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.521213173866272},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4829672873020172},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4629806578159332},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41908949613571167},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.34368306398391724}],"concepts":[{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.8035598993301392},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7879117727279663},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.5872225165367126},{"id":"https://openalex.org/C152478114","wikidata":"https://www.wikidata.org/wiki/Q660910","display_name":"Neurophysiology","level":2,"score":0.5833229422569275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5821690559387207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5671695470809937},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5546452403068542},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.5437227487564087},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.521213173866272},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4829672873020172},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4629806578159332},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41908949613571167},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.34368306398391724},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C129307140","wikidata":"https://www.wikidata.org/wiki/Q6795880","display_name":"Maximum bubble pressure method","level":3,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C157915830","wikidata":"https://www.wikidata.org/wiki/Q2928001","display_name":"Bubble","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/metroxraine54828.2022.9967496","is_oa":false,"landing_page_url":"https://doi.org/10.1109/metroxraine54828.2022.9967496","pdf_url":null,"source":{"id":"https://openalex.org/S4363608343","display_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.ubn.ru.nl:2066/285145","is_oa":false,"landing_page_url":"https://repository.ubn.ru.nl/handle/2066/285145","pdf_url":null,"source":{"id":"https://openalex.org/S4306401067","display_name":"Radboud Repository (Radboud University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I145872427","host_organization_name":"Radboud University Nijmegen","host_organization_lineage":["https://openalex.org/I145872427"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://hal.inria.fr/hal-03878615v1","raw_type":"Article in monograph or in proceedings"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1857789879","https://openalex.org/W1965769973","https://openalex.org/W2098391699","https://openalex.org/W2119163516","https://openalex.org/W2132360759","https://openalex.org/W2345279893","https://openalex.org/W2523246573","https://openalex.org/W2559463885","https://openalex.org/W2741907166","https://openalex.org/W2794345050","https://openalex.org/W2804957865","https://openalex.org/W2963355311","https://openalex.org/W2963587345","https://openalex.org/W3034996520","https://openalex.org/W3101658985","https://openalex.org/W3102455230","https://openalex.org/W3104324110","https://openalex.org/W3177342940","https://openalex.org/W3197209004","https://openalex.org/W4295312788","https://openalex.org/W6679555981","https://openalex.org/W6727249380","https://openalex.org/W6766978945"],"related_works":["https://openalex.org/W3202969339","https://openalex.org/W4237513258","https://openalex.org/W2044053727","https://openalex.org/W1994410349","https://openalex.org/W3177028067","https://openalex.org/W1913385466","https://openalex.org/W2914170859","https://openalex.org/W2889342546","https://openalex.org/W2015048155","https://openalex.org/W3153926117"],"abstract_inverted_index":{"Neurophysiological":[0],"time-series":[1],"recordings":[2],"of":[3,43,71,128,131,150,173,250],"brain":[4,41],"activity":[5],"like":[6,74],"the":[7,39,44,63,99,129,148,171,193,196,207,247,251,271,277,294,305,309,318,329,334,350],"electroencephalogram":[8],"(EEG)":[9],"or":[10,31,35,77,86,105,116],"local":[11],"field":[12],"potentials":[13],"can":[14,156,226],"be":[15,157,228],"decoded":[16],"by":[17,57,261,276,308],"machine":[18,72,201],"learning":[19,73,124,190,192,202],"models":[20],"in":[21,47,89,233,298],"order":[22,234],"to":[23,36,68,83,92,119,166,169,214,235,285,353],"either":[24,93],"control":[25],"an":[26,321],"application,":[27],"e.g.,":[28,46],"for":[29,143,175,239,320],"communication":[30],"rehabilitation":[32],"after":[33],"stroke,":[34],"passively":[37],"monitor":[38],"ongoing":[40],"state":[42],"subject,":[45],"a":[48,58,95,121,182,221,266,299],"demanding":[49],"work":[50],"environment.":[51],"A":[52],"typical":[53],"decoding":[54],"challenge":[55],"faced":[56],"brain-computer":[59],"interface":[60],"(BCI)":[61],"is":[62,125],"small":[64,101],"dataset":[65],"size":[66],"compared":[67,284],"other":[69,113],"domains":[70],"computer":[75],"vision":[76],"natural":[78],"language":[79],"processing.":[80],"The":[81],"possibilities":[82],"tackle":[84],"classification":[85],"regression":[87,279],"problems":[88],"BCI":[90,154,167],"are":[91],"train":[94,120],"regular":[96],"model":[97],"on":[98,206,246],"available":[100],"training":[102,262],"data":[103,111,223,338,348],"sets":[104],"through":[106],"transfer":[107],"learning,":[108],"which":[109,219],"utilizes":[110],"from":[112,195],"sessions,":[114],"subjects,":[115,218,316],"even":[117],"datasets":[118],"model.":[122],"Transfer":[123],"non-trivial":[126],"because":[127],"non-stationary":[130,314],"EEG":[132,176],"signals":[133],"between":[134],"subjects":[135,249],"but":[136],"also":[137,326],"within":[138],"subjects.":[139],"This":[140,210],"variability":[141],"calls":[142],"explicit":[144],"calibration":[145,205,240,292,331,347],"phases":[146],"at":[147],"start":[149],"every":[151],"session,":[152],"before":[153],"applications":[155],"used":[158],"online.":[159],"In":[160,178],"this":[161],"study,":[162],"we":[163,180],"present":[164],"arguments":[165],"researchers":[168],"encourage":[170],"use":[172],"embeddings":[174,194,216,343],"decoding.":[177],"particular,":[179],"introduce":[181],"simple":[183],"domain":[184],"adaptation":[185],"technique":[186,211],"involving":[187],"both":[188],"deep":[189],"(when":[191],"source":[197],"data)":[198],"and":[199,270,293],"classical":[200],"(for":[203],"fast":[204],"target":[208],"data).":[209],"allows":[212],"us":[213],"learn":[215],"across":[217,315],"deliver":[220],"generalized":[222],"representation.":[224],"These":[225],"then":[227],"fed":[229],"into":[230],"subject-specific":[231,291,323,330],"classifiers":[232],"minimize":[236],"their":[237],"need":[238,319],"data.":[241],"We":[242,302,325],"conducted":[243],"offline":[244],"experiments":[245],"14":[248],"High":[252],"Gamma":[253],"EEG-BCI":[254],"Dataset":[255],"[1].":[256],"Embedding":[257],"functions":[258,311],"were":[259,274,312],"obtained":[260],"EEGNet":[263,289],"[2]":[264],"using":[265],"leave-one-subject-out":[267],"(LOSO)":[268],"protocol,":[269],"embedding":[272,310],"vectors":[273],"classified":[275],"logistic":[278],"algorithm.":[280],"Our":[281],"pipeline":[282,297],"was":[283],"two":[286],"baseline":[287,352],"approaches:":[288],"without":[290],"standard":[295],"FBCSP":[296,351],"within-subject":[300],"training.":[301],"observed":[303,327],"that":[304,328,340],"representations":[306],"learned":[307],"indeed":[313,332],"justifying":[317],"additional":[322],"calibration.":[324],"improved":[333],"score.":[335],"Finally,":[336],"our":[337],"suggest,":[339],"building":[341],"upon":[342],"requires":[344],"fewer":[345],"individual":[346],"than":[349],"reach":[354],"satisfactory":[355],"scores.":[356]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
