{"id":"https://openalex.org/W2556600778","doi":"https://doi.org/10.4018/ijssci.2016070101","title":"Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods","display_name":"Improving Accuracy of Event-Related Potentials Classification by Channel Selection Using Independent Component Analysis and Least Square Methods","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2556600778","doi":"https://doi.org/10.4018/ijssci.2016070101","mag":"2556600778"},"language":"en","primary_location":{"id":"doi:10.4018/ijssci.2016070101","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijssci.2016070101","pdf_url":null,"source":{"id":"https://openalex.org/S201241086","display_name":"International Journal of Software Science and Computational Intelligence","issn_l":"1942-9037","issn":["1942-9037","1942-9045"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Software Science and Computational Intelligence","raw_type":"journal-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/A5114860638","display_name":"Wenxuan Li","orcid":"https://orcid.org/0009-0009-7720-7257"},"institutions":[{"id":"https://openalex.org/I118839592","display_name":"California State University, Bakersfield","ror":"https://ror.org/019ts0j55","country_code":"US","type":"education","lineage":["https://openalex.org/I118839592"]},{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"Wenxuan Li","raw_affiliation_strings":["School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin, China & Department of Computer and Electrical Engineering and Computer Science, California State University, Bakersfield, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China & Department of Computer and Electrical Engineering and Computer Science, California State University, Bakersfield, CA, USA","institution_ids":["https://openalex.org/I118839592","https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100713104","display_name":"Mengfan Li","orcid":"https://orcid.org/0000-0003-1501-9516"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengfan Li","raw_affiliation_strings":["School of Electrical Engineering and Automation, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100318377","display_name":"Wei Li","orcid":"https://orcid.org/0000-0003-1418-0201"},"institutions":[{"id":"https://openalex.org/I118839592","display_name":"California State University, Bakersfield","ror":"https://ror.org/019ts0j55","country_code":"US","type":"education","lineage":["https://openalex.org/I118839592"]},{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","School of Electrical Engineering and Automation, Tianjin University, Tianjin, China & Department of Computer and Electrical Engineering and Computer Science, California State University, Bakersfield, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]},{"raw_affiliation_string":"School of Electrical Engineering and Automation, Tianjin University, Tianjin, China & Department of Computer and Electrical Engineering and Computer Science, California State University, Bakersfield, CA, USA","institution_ids":["https://openalex.org/I118839592","https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5114860638"],"corresponding_institution_ids":["https://openalex.org/I118839592","https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.5044,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.68039393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"8","issue":"3","first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9998999834060669,"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":0.9998999834060669,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10581","display_name":"Neural dynamics and brain function","score":0.9943000078201294,"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/computer-science","display_name":"Computer science","score":0.7689808011054993},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.7504100799560547},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6035665273666382},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.584983766078949},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5309811234474182},{"id":"https://openalex.org/keywords/component-analysis","display_name":"Component analysis","score":0.5164661407470703},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.44794631004333496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.43264082074165344},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39333632588386536},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35791704058647156},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.06055691838264465}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7689808011054993},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.7504100799560547},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6035665273666382},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.584983766078949},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5309811234474182},{"id":"https://openalex.org/C2780692498","wikidata":"https://www.wikidata.org/wiki/Q16950721","display_name":"Component analysis","level":2,"score":0.5164661407470703},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.44794631004333496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43264082074165344},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39333632588386536},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35791704058647156},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.06055691838264465},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijssci.2016070101","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijssci.2016070101","pdf_url":null,"source":{"id":"https://openalex.org/S201241086","display_name":"International Journal of Software Science and Computational Intelligence","issn_l":"1942-9037","issn":["1942-9037","1942-9045"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Software Science and Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1536620489","https://openalex.org/W1990928820","https://openalex.org/W2004961743","https://openalex.org/W2038016568","https://openalex.org/W2067043925","https://openalex.org/W2068921343","https://openalex.org/W2072164240","https://openalex.org/W2074165061","https://openalex.org/W2076758484","https://openalex.org/W2079277602","https://openalex.org/W2092649222","https://openalex.org/W2094017272","https://openalex.org/W2103032521","https://openalex.org/W2117041729","https://openalex.org/W2120879299","https://openalex.org/W2130819666","https://openalex.org/W2138964882","https://openalex.org/W2141330748","https://openalex.org/W2141335492","https://openalex.org/W2145302786","https://openalex.org/W2169866873","https://openalex.org/W2183630776","https://openalex.org/W2347108255","https://openalex.org/W2507937192"],"related_works":["https://openalex.org/W1971575144","https://openalex.org/W2369494890","https://openalex.org/W2139404519","https://openalex.org/W2123927273","https://openalex.org/W2121025724","https://openalex.org/W2393502243","https://openalex.org/W3112853371","https://openalex.org/W2370924545","https://openalex.org/W2897887562","https://openalex.org/W3105707994"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,7],"method":[4,78,82,109],"for":[5],"achieving":[6],"high":[8],"performance":[9],"of":[10,53,66,114],"N200":[11,30],"and":[12,31,34,50,57,79,96],"P300":[13,32],"classification":[14],"by":[15],"applying":[16],"independent":[17,55],"component":[18],"analysis":[19],"to":[20,61,122],"select":[21],"the":[22,40,44,47,51,54,63,76,80,87,90,107,123],"channels,":[23],"which":[24,119],"deliver":[25],"brain":[26],"signals":[27],"with":[28],"large":[29],"potentials":[33],"small":[35],"artifacts.":[36],"In":[37],"this":[38,59,85],"study,":[39],"authors":[41],"find":[42],"out":[43],"relationship":[45,60],"between":[46],"highest":[48],"accuracy":[49,113],"weights":[52],"components":[56],"use":[58],"predict":[62],"optimal":[64,92],"channels":[65],"each":[67],"individual":[68],"subject.":[69],"They":[70],"compare":[71],"five":[72],"channel":[73,94,99],"selection":[74],"methods:":[75],"ICA-based":[77,108],"curve-fitting-based":[81],"proposed":[83],"in":[84],"paper,":[86],"amplitude-based":[88],"method,":[89],"experiential":[91],"8":[93],"combination":[95,100],"all":[97],"30":[98],"methods.":[101,126],"The":[102],"comparative":[103],"studies":[104],"show":[105],"that":[106],"achieves":[110],"an":[111],"average":[112],"99.3%":[115],"across":[116],"four":[117,125],"subjects,":[118],"is":[120],"superior":[121],"other":[124]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
