{"id":"https://openalex.org/W4395090246","doi":"https://doi.org/10.1109/southeastcon52093.2024.10500162","title":"Transfer Learning for P300 Speller: An Alignment Approach for Cross-Subject P300 Data","display_name":"Transfer Learning for P300 Speller: An Alignment Approach for Cross-Subject P300 Data","publication_year":2024,"publication_date":"2024-03-15","ids":{"openalex":"https://openalex.org/W4395090246","doi":"https://doi.org/10.1109/southeastcon52093.2024.10500162"},"language":"en","primary_location":{"id":"doi:10.1109/southeastcon52093.2024.10500162","doi":"https://doi.org/10.1109/southeastcon52093.2024.10500162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/southeastcon52093.2024.10500162","pdf_url":null,"source":{"id":"https://openalex.org/S4393917016","display_name":"IEEE SoutheastCon","issn_l":"1091-0050","issn":["1091-0050"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"SoutheastCon 2024","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5002887525","display_name":"Maohua Liu","orcid":"https://orcid.org/0009-0005-4032-7555","observed_orcids":["https://orcid.org/0009-0005-4032-7555"]},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maohua Liu","raw_affiliation_strings":["School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743410","display_name":"Faraz Ahmad","orcid":"https://orcid.org/0000-0002-0405-9910","observed_orcids":["https://orcid.org/0000-0002-0405-9910"]},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Faraz Ahmad","raw_affiliation_strings":["School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602","institution_ids":["https://openalex.org/I165733156"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036344518","display_name":"Fred R. Beyette","orcid":"https://orcid.org/0000-0001-8704-7810","observed_orcids":["https://orcid.org/0000-0001-8704-7810"]},"institutions":[{"id":"https://openalex.org/I165733156","display_name":"University of Georgia","ror":"https://ror.org/00te3t702","country_code":"US","type":"education","lineage":["https://openalex.org/I165733156"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fred R. Beyette","raw_affiliation_strings":["School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, College of Engineering, University of Georgia,Athens,USA,30602","institution_ids":["https://openalex.org/I165733156"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165733156"],"apc_list":null,"apc_paid":null,"fwci":1.5709,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.79197496,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1438","last_page":"1443"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9984999895095825,"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":0.9984999895095825,"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/T11707","display_name":"Gaze Tracking and Assistive Technology","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.0,"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/p300-speller","display_name":"P300 speller","score":0.996999979019165},{"id":"https://openalex.org/keywords/transfer-learning","display_name":"transfer learning","score":0.7710000276565552},{"id":"https://openalex.org/keywords/cross-subject-transfer","display_name":"cross-subject transfer","score":0.75},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"EEG","score":0.5799999833106995},{"id":"https://openalex.org/keywords/time-series-alignment","display_name":"time series alignment","score":0.8399999737739563},{"id":"https://openalex.org/keywords/subject-variability","display_name":"subject variability","score":0.8679999709129333},{"id":"https://openalex.org/keywords/brain-computer-interface","display_name":"brain-computer interface","score":0.7940000295639038}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.737535834312439},{"id":"https://openalex.org/C173201364","wikidata":"https://www.wikidata.org/wiki/Q897410","display_name":"Brain\u2013computer interface","level":3,"score":0.6759235858917236},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6394011974334717},{"id":"https://openalex.org/C2777855551","wikidata":"https://www.wikidata.org/wiki/Q12310021","display_name":"Subject (documents)","level":2,"score":0.6061412692070007},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.5455420017242432},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.45231926441192627},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.378031849861145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3759935200214386},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11838650703430176},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11240410804748535},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.10985219478607178},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.08293208479881287},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.07785877585411072}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/southeastcon52093.2024.10500162","doi":"https://doi.org/10.1109/southeastcon52093.2024.10500162","is_oa":false,"landing_page_url":"https://doi.org/10.1109/southeastcon52093.2024.10500162","pdf_url":null,"source":{"id":"https://openalex.org/S4393917016","display_name":"IEEE SoutheastCon","issn_l":"1091-0050","issn":["1091-0050"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"SoutheastCon 2024","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1973574316","https://openalex.org/W1996167318","https://openalex.org/W2129023315","https://openalex.org/W2135400163","https://openalex.org/W2165698076","https://openalex.org/W2559463885","https://openalex.org/W2746829572","https://openalex.org/W2831752284","https://openalex.org/W2963727766","https://openalex.org/W3019053383","https://openalex.org/W3133048813","https://openalex.org/W4245692952","https://openalex.org/W4386763635","https://openalex.org/W4389724359","https://openalex.org/W4390401429","https://openalex.org/W6695684050"],"related_works":["https://openalex.org/W4395067409","https://openalex.org/W4412926136","https://openalex.org/W7117889487","https://openalex.org/W4404007267","https://openalex.org/W4377226328","https://openalex.org/W7083309651","https://openalex.org/W3009134349","https://openalex.org/W7160446816","https://openalex.org/W3082326180","https://openalex.org/W4313026961"],"abstract_inverted_index":{"The":[0,124,137],"P300":[1,38,80,176],"speller":[2],"can":[3,15,170],"bridge":[4],"brain":[5],"to":[6,25,34,64,109,143],"surrounding":[7],"devices":[8],"by":[9],"analyzing":[10],"electroencephalogram":[11],"(EEG)":[12],"signals.":[13],"It":[14],"help":[16],"people":[17],"who":[18],"lost":[19],"ability":[20,108],"of":[21,37,152,174],"moving":[22],"or":[23],"speaking":[24],"re-establish":[26],"their":[27],"communication":[28],"with":[29],"the":[30,59,85,160,172],"world.":[31],"However,":[32],"owing":[33],"subject":[35,122,132],"variability":[36,133],"wave,":[39],"transfer":[40,77],"learning":[41,78],"accuracy":[42],"is":[43,49,163],"not":[44],"ideal":[45],"and":[46,146],"considerable":[47],"time":[48,101,167,186],"spent":[50],"on":[51],"calibration":[52,145],"when":[53],"switching":[54],"users,":[55],"which":[56],"significantly":[57],"affects":[58],"user":[60],"experience.":[61],"In":[62],"response":[63],"these":[65],"challenges,":[66],"this":[67],"study":[68],"introduces":[69],"a":[70,96,116],"groundbreaking":[71],"data":[72,111],"alignment":[73],"approach":[74,88,105,139,162],"tailored":[75],"for":[76,90,99,165],"in":[79,130,157],"spellers.":[81],"Drawing":[82],"inspiration":[83],"from":[84,112],"Euclidean":[86],"Alignment":[87],"used":[89],"aligning":[91,100,153,166],"covariance":[92,154],"matrices,":[93],"we":[94],"developed":[95],"unique":[97],"method":[98],"series":[102,187],"data.":[103],"Our":[104],"demonstrates":[106],"its":[107,128],"bring":[110],"diverse":[113],"subjects":[114],"into":[115],"more":[117,175],"uniform":[118],"distribution,":[119],"effectively":[120],"minimizing":[121],"variability.":[123],"experimental":[125],"results":[126],"affirm":[127],"effectiveness":[129],"reducing":[131],"while":[134],"maintaining":[135],"efficiency.":[136],"proposed":[138,161],"opens":[140],"another":[141],"pathway":[142],"bypass":[144],"has":[147],"untapped":[148],"potential.":[149],"Additionally,":[150],"instead":[151],"matrices":[155],"as":[156,179,188],"existing":[158],"approaches,":[159],"designed":[164],"series,":[168],"thereby":[169],"meet":[171],"need":[173],"classifiers":[177],"such":[178],"Convolutional":[180],"Neural":[181],"Networks":[182],"(CNN)":[183],"that":[184],"takes":[185],"input.":[189]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2026-10-26T11:51:55.731654","created_date":"2024-04-25T00:00:00"}
