{"id":"https://openalex.org/W4416799209","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249132","title":"Outlier Removal in MEG Data for Imagined Speech Classification","display_name":"Outlier Removal in MEG Data for Imagined Speech Classification","publication_year":2025,"publication_date":"2025-10-22","ids":{"openalex":"https://openalex.org/W4416799209","doi":"https://doi.org/10.1109/apsipaasc65261.2025.11249132"},"language":null,"primary_location":{"id":"doi:10.1109/apsipaasc65261.2025.11249132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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":null,"display_name":"Koki Nose","orcid":null},"institutions":[{"id":"https://openalex.org/I65837984","display_name":"Kobe University","ror":"https://ror.org/03tgsfw79","country_code":"JP","type":"education","lineage":["https://openalex.org/I65837984"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Koki Nose","raw_affiliation_strings":["Graduate School of System Informatics, Kobe University,Kobe,Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of System Informatics, Kobe University,Kobe,Japan","institution_ids":["https://openalex.org/I65837984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053136013","display_name":"Hajime Yano","orcid":"https://orcid.org/0000-0002-1615-7526"},"institutions":[{"id":"https://openalex.org/I65837984","display_name":"Kobe University","ror":"https://ror.org/03tgsfw79","country_code":"JP","type":"education","lineage":["https://openalex.org/I65837984"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hajime Yano","raw_affiliation_strings":["Graduate School of System Informatics, Kobe University,Kobe,Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of System Informatics, Kobe University,Kobe,Japan","institution_ids":["https://openalex.org/I65837984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009283470","display_name":"Tetsuya Takiguchi","orcid":"https://orcid.org/0000-0001-5005-7679"},"institutions":[{"id":"https://openalex.org/I65837984","display_name":"Kobe University","ror":"https://ror.org/03tgsfw79","country_code":"JP","type":"education","lineage":["https://openalex.org/I65837984"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Takiguchi","raw_affiliation_strings":["Graduate School of System Informatics, Kobe University,Kobe,Japan"],"affiliations":[{"raw_affiliation_string":"Graduate School of System Informatics, Kobe University,Kobe,Japan","institution_ids":["https://openalex.org/I65837984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070976916","display_name":"Seiji Nakagawa","orcid":"https://orcid.org/0000-0002-9957-325X"},"institutions":[{"id":"https://openalex.org/I159385669","display_name":"Chiba University","ror":"https://ror.org/01hjzeq58","country_code":"JP","type":"education","lineage":["https://openalex.org/I159385669"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seiji Nakagawa","raw_affiliation_strings":["Center for Frontier Medical Engineering, Chiba University,Chiba,Japan"],"affiliations":[{"raw_affiliation_string":"Center for Frontier Medical Engineering, Chiba University,Chiba,Japan","institution_ids":["https://openalex.org/I159385669"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I65837984"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35952499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1435","last_page":"1440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.7228999733924866,"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":0.7228999733924866,"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":0.07169999927282333,"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"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.04430000111460686,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.8113999962806702},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.6886000037193298},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6218000054359436},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6200000047683716},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5849999785423279},{"id":"https://openalex.org/keywords/magnetoencephalography","display_name":"Magnetoencephalography","score":0.5842999815940857},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5163999795913696},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.5041999816894531}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.8113999962806702},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.6886000037193298},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6784999966621399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6496999859809875},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6200000047683716},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5849999785423279},{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.5842999815940857},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5163999795913696},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.5041999816894531},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.37709999084472656},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.30309998989105225},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.26260000467300415}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipaasc65261.2025.11249132","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipaasc65261.2025.11249132","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8895786939","display_name":null,"funder_award_id":"JP22K18423,JP25K00660","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1990746954","https://openalex.org/W2084333685","https://openalex.org/W2169918686","https://openalex.org/W2559463885","https://openalex.org/W2739095506","https://openalex.org/W2754920576","https://openalex.org/W3180271786","https://openalex.org/W4310375797","https://openalex.org/W4380784998","https://openalex.org/W4381162286","https://openalex.org/W4384203497"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"proposed":[2,122],"an":[3],"outlier":[4,53,175],"removal":[5],"method":[6,123],"for":[7],"magnetoencephalography":[8],"(MEG)":[9],"data":[10,42,63,142,166],"during":[11],"auditory":[12],"imagery":[13],"tasks":[14],"to":[15,48,80,130,151,158],"improve":[16],"classification":[17,125],"accuracy.":[18],"While":[19],"previous":[20],"studies":[21],"on":[22,30,61,69,104,141,145,169],"imagined":[23,182],"speech":[24,183],"brain-computer":[25],"interfaces":[26],"(BCIs)":[27],"mainly":[28],"relied":[29],"electroencephalography":[31],"(EEG),":[32],"MEG":[33,62,86],"offered":[34],"higher":[35],"spatial":[36],"resolution":[37],"but":[38],"suffered":[39],"from":[40,107],"smaller":[41],"sizes,":[43],"making":[44],"it":[45],"more":[46],"sensitive":[47],"abnormal":[49],"trials.":[50],"To":[51],"address":[52],"removal,":[54],"we":[55,74],"first":[56],"pretrained":[57],"a":[58,94],"feature":[59,78,82,109,139],"extractor":[60,79,140],"that":[64,120,165],"had":[65],"been":[66],"augmented":[67,143],"based":[68,103,144,168],"current":[70,146,170],"source":[71,147,171],"estimation.":[72],"Second,":[73],"applied":[75,129],"the":[76,85,108,121,138,159,178],"trained":[77],"obtain":[81],"representations":[83],"of":[84,180],"samples.":[87],"Third,":[88],"these":[89],"features":[90],"were":[91,100,112],"projected":[92],"into":[93],"low-dimensional":[95],"space":[96],"using":[97],"t-SNE.":[98],"Outliers":[99],"then":[101],"identified":[102],"their":[105],"deviation":[106],"distribution":[110],"and":[111,133,173],"removed":[113],"before":[114],"classifier":[115],"retraining.":[116],"Experimental":[117],"results":[118],"demonstrate":[119],"improved":[124],"accuracy,":[126],"particularly":[127],"when":[128],"both":[131],"training":[132,137],"validation":[134],"data.":[135],"Furthermore,":[136],"estimation":[148,172],"likely":[149],"led":[150],"clearer":[152],"class":[153],"boundaries,":[154],"which":[155],"further":[156],"contributed":[157],"performance":[160],"improvement.":[161],"These":[162],"findings":[163],"suggest":[164],"augmentation":[167],"t-SNE-based":[174],"detection":[176],"enhance":[177],"robustness":[179],"MEG-based":[181],"decoding.":[184]},"counts_by_year":[],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-11-28T00:00:00"}