{"id":"https://openalex.org/W4393407160","doi":"https://doi.org/10.1109/bci60775.2024.10480489","title":"Channel-optimized Local Region Riemannian Approach for Motor Imagery Classification","display_name":"Channel-optimized Local Region Riemannian Approach for Motor Imagery Classification","publication_year":2024,"publication_date":"2024-02-26","ids":{"openalex":"https://openalex.org/W4393407160","doi":"https://doi.org/10.1109/bci60775.2024.10480489"},"language":"en","primary_location":{"id":"doi:10.1109/bci60775.2024.10480489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci60775.2024.10480489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 12th International Winter Conference on Brain-Computer Interface (BCI)","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/A5058211461","display_name":"Jinhyo Shin","orcid":"https://orcid.org/0009-0000-2028-2285"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Jinhyo Shin","raw_affiliation_strings":["Korea University,Dept. Artificial Intelligence,Seoul,Republic of Korea","Dept. Artificial Intelligence, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. Artificial Intelligence,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Dept. Artificial Intelligence, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023585249","display_name":"Wonzoo Chung","orcid":"https://orcid.org/0000-0001-7381-250X"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Wonzoo Chung","raw_affiliation_strings":["Korea University,Dept. Artificial Intelligence,Seoul,Republic of Korea","Dept. Artificial Intelligence, Korea University, Seoul, Republic of Korea"],"affiliations":[{"raw_affiliation_string":"Korea University,Dept. Artificial Intelligence,Seoul,Republic of Korea","institution_ids":["https://openalex.org/I197347611"]},{"raw_affiliation_string":"Dept. Artificial Intelligence, Korea University, Seoul, Republic of Korea","institution_ids":["https://openalex.org/I197347611"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5058211461"],"corresponding_institution_ids":["https://openalex.org/I197347611"],"apc_list":null,"apc_paid":null,"fwci":0.7252,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.73260873,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9491000175476074,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9491000175476074,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9217000007629395,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5128827095031738},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.44926947355270386},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44023704528808594},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4077921211719513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3228985071182251},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.1849144995212555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5128827095031738},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.44926947355270386},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44023704528808594},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4077921211719513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3228985071182251},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.1849144995212555}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bci60775.2024.10480489","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bci60775.2024.10480489","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 12th International Winter Conference on Brain-Computer Interface (BCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321373","display_name":"Korea University","ror":"https://ror.org/047dqcg40"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2041998778","https://openalex.org/W2096597330","https://openalex.org/W2101629643","https://openalex.org/W2116022929","https://openalex.org/W2132360759","https://openalex.org/W2142280324","https://openalex.org/W2338546148","https://openalex.org/W2540507509","https://openalex.org/W2789407924","https://openalex.org/W2953384096","https://openalex.org/W3139270893","https://openalex.org/W4210245176","https://openalex.org/W4226402870","https://openalex.org/W4285206118"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,96,104,113,128,132,159],"novel":[6],"motor":[7],"imagery":[8],"(MI)":[9],"classification":[10,27,196],"method":[11,31,72,166],"that":[12,62,98,183],"uses":[13],"channel":[14,36,59,64],"optimization":[15,185],"within":[16,47],"local":[17,33,53,74,86,123,133,187],"region":[18,49,124,188],"and":[19,42,66,76,131,156,179,193],"Riemannian":[20,54,105,109],"approach":[21],"with":[22],"filter":[23,129],"bank":[24],"to":[25,50,91,152,158,199],"improve":[26],"performance.":[28],"The":[29,117,161],"proposed":[30,71,165],"establishes":[32],"regions":[34],"as":[35],"subsets":[37],"consisting":[38],"of":[39,82,103,163,186],"adjacent":[40],"channels":[41,46,68,83,189],"selects":[43],"the":[44,70,100,147,153,164,171,180,184,191,200],"optimal":[45],"each":[48,121,139],"generate":[51],"discriminative":[52],"features.":[55],"Unlike":[56],"traditional":[57],"global":[58],"selection":[60],"methods":[61],"ignore":[63],"location":[65],"discard":[67],"completely,":[69],"preserves":[73],"information":[75],"allows":[77],"selective":[78],"inclusion":[79],"or":[80],"exclusion":[81],"in":[84],"different":[85],"regions.":[87],"Channels":[88],"are":[89,125,143],"optimized":[90],"minimize":[92],"confusion":[93,148],"area":[94,149],"score,":[95,150],"criterion":[97],"assesses":[99],"misclassification":[101],"risk":[102],"feature":[106],"based":[107,145],"on":[108,146],"distance":[110],"distributions,":[111],"using":[112,127,170],"backward":[114],"iterative":[115],"algorithm.":[116],"EEG":[118],"signals":[119],"from":[120],"channel-optimized":[122],"decomposed":[126],"bank,":[130],"covariance":[134],"matrix":[135],"is":[136],"computed":[137],"for":[138],"subband.":[140],"Several":[141],"matrices":[142],"selected":[144],"mapped":[151],"tangent":[154],"space,":[155],"fed":[157],"classifier.":[160],"performance":[162,192],"has":[167],"been":[168],"evaluated":[169],"publicly":[172],"available":[173],"BCI":[174],"Competition":[175],"III":[176],"dataset":[177],"IVa,":[178],"results":[181],"show":[182],"improves":[190],"achieves":[194],"higher":[195],"accuracy":[197],"compared":[198],"related":[201],"existing":[202],"methods.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
