{"id":"https://openalex.org/W4286306260","doi":"https://doi.org/10.23919/ascc56756.2022.9828295","title":"Classification of six sound categories using functional near-infrared spectroscopy","display_name":"Classification of six sound categories using functional near-infrared spectroscopy","publication_year":2022,"publication_date":"2022-05-04","ids":{"openalex":"https://openalex.org/W4286306260","doi":"https://doi.org/10.23919/ascc56756.2022.9828295"},"language":"en","primary_location":{"id":"doi:10.23919/ascc56756.2022.9828295","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ascc56756.2022.9828295","pdf_url":null,"source":{"id":"https://openalex.org/S4363607827","display_name":"2022 13th Asian Control Conference (ASCC)","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 13th Asian Control Conference (ASCC)","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/A5071615510","display_name":"So-Hyeon Yoo","orcid":"https://orcid.org/0000-0001-7442-2511"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"So-Hyeon Yoo","raw_affiliation_strings":["Pusan National University,Department of Mechanical Engineering,Busan","Department of Mechanical Engineering, Pusan National University, Busan"],"affiliations":[{"raw_affiliation_string":"Pusan National University,Department of Mechanical Engineering,Busan","institution_ids":["https://openalex.org/I4921948"]},{"raw_affiliation_string":"Department of Mechanical Engineering, Pusan National University, Busan","institution_ids":["https://openalex.org/I4921948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021609168","display_name":"Keum\u2010Shik Hong","orcid":"https://orcid.org/0000-0002-8528-4457"},"institutions":[{"id":"https://openalex.org/I4921948","display_name":"Pusan National University","ror":"https://ror.org/01an57a31","country_code":"KR","type":"education","lineage":["https://openalex.org/I4921948"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Keum-Shik Hong","raw_affiliation_strings":["Pusan National University,Department of Mechanical Engineering,Busan","Department of Mechanical Engineering, Pusan National University, Busan"],"affiliations":[{"raw_affiliation_string":"Pusan National University,Department of Mechanical Engineering,Busan","institution_ids":["https://openalex.org/I4921948"]},{"raw_affiliation_string":"Department of Mechanical Engineering, Pusan National University, Busan","institution_ids":["https://openalex.org/I4921948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071615510"],"corresponding_institution_ids":["https://openalex.org/I4921948"],"apc_list":null,"apc_paid":null,"fwci":0.3206,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42622209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9980000257492065,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7794994115829468},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.750381350517273},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7000115513801575},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6902239322662354},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6274974346160889},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6103411912918091},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.528983473777771},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.504593014717102},{"id":"https://openalex.org/keywords/functional-near-infrared-spectroscopy","display_name":"Functional near-infrared spectroscopy","score":0.4917748272418976},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.46844011545181274},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.43527528643608093},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.41724711656570435},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.1267595887184143}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7794994115829468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.750381350517273},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7000115513801575},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6902239322662354},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6274974346160889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6103411912918091},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.528983473777771},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.504593014717102},{"id":"https://openalex.org/C130796691","wikidata":"https://www.wikidata.org/wiki/Q750537","display_name":"Functional near-infrared spectroscopy","level":4,"score":0.4917748272418976},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.46844011545181274},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.43527528643608093},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.41724711656570435},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.1267595887184143},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C2781195155","wikidata":"https://www.wikidata.org/wiki/Q18680","display_name":"Prefrontal cortex","level":3,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/ascc56756.2022.9828295","is_oa":false,"landing_page_url":"https://doi.org/10.23919/ascc56756.2022.9828295","pdf_url":null,"source":{"id":"https://openalex.org/S4363607827","display_name":"2022 13th Asian Control Conference (ASCC)","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 13th Asian Control Conference (ASCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.699999988079071,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320335199","display_name":"Korea Institute of Energy Technology Evaluation and Planning","ror":"https://ror.org/02zq38y32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1584452390","https://openalex.org/W1817561967","https://openalex.org/W1967512849","https://openalex.org/W1969364717","https://openalex.org/W1971453761","https://openalex.org/W1974893143","https://openalex.org/W2011111213","https://openalex.org/W2029633665","https://openalex.org/W2038737766","https://openalex.org/W2041440176","https://openalex.org/W2042055936","https://openalex.org/W2045561515","https://openalex.org/W2068904026","https://openalex.org/W2070230712","https://openalex.org/W2075647286","https://openalex.org/W2105737936","https://openalex.org/W2113250253","https://openalex.org/W2123927491","https://openalex.org/W2134456465","https://openalex.org/W2146089088","https://openalex.org/W2154533194","https://openalex.org/W2158485497","https://openalex.org/W2162898349","https://openalex.org/W2163771047","https://openalex.org/W2256369587","https://openalex.org/W2282299048","https://openalex.org/W2397546789","https://openalex.org/W2403091650","https://openalex.org/W2518226033","https://openalex.org/W2521873691","https://openalex.org/W2561272868","https://openalex.org/W2578865187","https://openalex.org/W2600751893","https://openalex.org/W2733268233","https://openalex.org/W2800350051","https://openalex.org/W2811153207","https://openalex.org/W2896238715","https://openalex.org/W2903404864","https://openalex.org/W2909619163","https://openalex.org/W2948303596","https://openalex.org/W2975662482","https://openalex.org/W2990878490","https://openalex.org/W3026272888","https://openalex.org/W3100120625","https://openalex.org/W3130935255","https://openalex.org/W3152338574","https://openalex.org/W3159420371","https://openalex.org/W3164793060","https://openalex.org/W3215527611","https://openalex.org/W6735201814"],"related_works":["https://openalex.org/W2113454941","https://openalex.org/W2951122819","https://openalex.org/W4288315282","https://openalex.org/W2411585343","https://openalex.org/W2753804328","https://openalex.org/W2085106164","https://openalex.org/W2089282851","https://openalex.org/W2092457881","https://openalex.org/W2999226773","https://openalex.org/W2044743710"],"abstract_inverted_index":{"Sound":[0],"category":[1],"recognition":[2],"is":[3],"essential":[4],"for":[5,120],"auditory":[6],"perception":[7],"in":[8,48],"the":[9,43,49,61,90,113,116],"temporal":[10],"cortex.":[11],"This":[12,110],"paper":[13],"aims":[14],"to":[15,33,59,88],"classify":[16],"six":[17],"different":[18],"sound":[19,64],"categories":[20],"based":[21],"on":[22],"functional":[23],"near-infrared":[24],"spectroscopy":[25],"(fNIRS).":[26],"Recursive":[27],"least":[28],"square":[29],"estimation":[30],"was":[31],"applied":[32,87],"remove":[34],"physiological":[35],"noises,":[36],"and":[37,81,106],"various":[38,67],"features":[39,53],"were":[40,54,86,96],"extracted":[41],"from":[42],"oxy-hemoglobin":[44],"(HbO)":[45],"activation":[46],"curve":[47],"time":[50],"domain.":[51],"Extracted":[52],"selected":[55],"by":[56],"statistical":[57],"method":[58],"maximize":[60],"differences":[62],"between":[63],"categories.":[65],"Furthermore,":[66],"classification":[68,94,118],"methods":[69],"(linear":[70],"discriminant":[71],"analysis":[72],"(LDA),":[73],"support":[74],"vector":[75],"machine":[76],"(SVM),":[77],"k-nearest":[78],"neighbor":[79],"(kNN),":[80],"na\u00efve":[82],"Bayes":[83],"classifier":[84],"(NB))":[85],"get":[89],"acceptable":[91],"accuracy.":[92],"The":[93],"accuracies":[95],"20.17%":[97],"with":[98,101,104,108],"LDA,":[99],"32.61%":[100],"SVM,":[102],"24.54%":[103],"kNN,":[105],"25.71%":[107],"NB.":[109],"work":[111],"demonstrates":[112],"potentiality":[114],"of":[115],"online":[117],"process":[119],"decoding":[121],"what":[122],"people":[123],"hear":[124],"using":[125],"fNIRS.":[126]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
