{"id":"https://openalex.org/W2775009081","doi":"https://doi.org/10.1109/mfi.2017.8170427","title":"fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network","display_name":"fNIRS motion artifact correction for overground walking using entropy based unbalanced optode decision and wavelet regression neural network","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2775009081","doi":"https://doi.org/10.1109/mfi.2017.8170427","mag":"2775009081"},"language":"en","primary_location":{"id":"doi:10.1109/mfi.2017.8170427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2017.8170427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","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/A5060383586","display_name":"Gihyoun Lee","orcid":"https://orcid.org/0000-0002-0969-4884"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Gihyoun Lee","raw_affiliation_strings":["DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045212808","display_name":"Sang Hyeon Jin","orcid":"https://orcid.org/0000-0001-8630-6120"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sang Hyeon Jin","raw_affiliation_strings":["DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I193352282"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100407263","display_name":"Seung Hyun Lee","orcid":"https://orcid.org/0000-0002-1109-6787"},"institutions":[{"id":"https://openalex.org/I31419693","display_name":"Kyungpook National University","ror":"https://ror.org/040c17130","country_code":"KR","type":"education","lineage":["https://openalex.org/I31419693"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seung Hyun Lee","raw_affiliation_strings":["Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I31419693"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041371940","display_name":"Berdakh Abibullaev","orcid":"https://orcid.org/0000-0002-8623-5526"},"institutions":[{"id":"https://openalex.org/I60559429","display_name":"Nazarbayev University","ror":"https://ror.org/052bx8q98","country_code":"KZ","type":"education","lineage":["https://openalex.org/I60559429"]}],"countries":["KZ"],"is_corresponding":false,"raw_author_name":"Berdakh Abibullaev","raw_affiliation_strings":["Robotics Department, Nazarbayev University, Astana, Republic of Kazakhstan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robotics Department, Nazarbayev University, Astana, Republic of Kazakhstan","institution_ids":["https://openalex.org/I60559429"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017309732","display_name":"Jinung An","orcid":"https://orcid.org/0000-0001-9041-3201"},"institutions":[{"id":"https://openalex.org/I193352282","display_name":"Daegu Gyeongbuk Institute of Science and Technology","ror":"https://ror.org/03frjya69","country_code":"KR","type":"education","lineage":["https://openalex.org/I193352282"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinung An","raw_affiliation_strings":["DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"DGIST, Convergence Research Center for Wellness, Daegu, Republic of Korea","institution_ids":["https://openalex.org/I193352282"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4387,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.68531824,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"186","last_page":"193"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10977","display_name":"Optical Imaging and Spectroscopy Techniques","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9993000030517578,"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/T11700","display_name":"Hemodynamic Monitoring and Therapy","score":0.9746999740600586,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7272031307220459},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7039516568183899},{"id":"https://openalex.org/keywords/functional-near-infrared-spectroscopy","display_name":"Functional near-infrared spectroscopy","score":0.6690770387649536},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.5831096768379211},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.5614446997642517},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5511882901191711},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.5384213328361511},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5026304721832275},{"id":"https://openalex.org/keywords/general-linear-model","display_name":"General linear model","score":0.4938119351863861},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.47584056854248047},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.326100617647171},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14388468861579895},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.109591543674469}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7272031307220459},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7039516568183899},{"id":"https://openalex.org/C130796691","wikidata":"https://www.wikidata.org/wiki/Q750537","display_name":"Functional near-infrared spectroscopy","level":4,"score":0.6690770387649536},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.5831096768379211},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5614446997642517},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5511882901191711},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.5384213328361511},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5026304721832275},{"id":"https://openalex.org/C188321436","wikidata":"https://www.wikidata.org/wiki/Q1826488","display_name":"General linear model","level":3,"score":0.4938119351863861},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.47584056854248047},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.326100617647171},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14388468861579895},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.109591543674469},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mfi.2017.8170427","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mfi.2017.8170427","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7599999904632568,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1528119265","https://openalex.org/W1554532831","https://openalex.org/W1645855841","https://openalex.org/W1979421079","https://openalex.org/W1980786684","https://openalex.org/W1991550546","https://openalex.org/W1995875735","https://openalex.org/W1996183177","https://openalex.org/W1998442441","https://openalex.org/W2002994039","https://openalex.org/W2019910180","https://openalex.org/W2040896410","https://openalex.org/W2058713030","https://openalex.org/W2080690725","https://openalex.org/W2081349230","https://openalex.org/W2091953004","https://openalex.org/W2092890379","https://openalex.org/W2098994855","https://openalex.org/W2102244548","https://openalex.org/W2106693625","https://openalex.org/W2134963684","https://openalex.org/W2139649098","https://openalex.org/W2146744682","https://openalex.org/W2158076175","https://openalex.org/W2169292331","https://openalex.org/W2170797424","https://openalex.org/W2323065399","https://openalex.org/W2462846337","https://openalex.org/W2498761961","https://openalex.org/W2519152515","https://openalex.org/W2587982668","https://openalex.org/W6632995402"],"related_works":["https://openalex.org/W1496205134","https://openalex.org/W1964522385","https://openalex.org/W2186087635","https://openalex.org/W2292302374","https://openalex.org/W4200237757","https://openalex.org/W4309298396","https://openalex.org/W2374143814","https://openalex.org/W1977326001","https://openalex.org/W3115969243","https://openalex.org/W1502810620"],"abstract_inverted_index":{"Functional":[0],"near-infrared":[1,16],"spectroscopy":[2],"(fNIRS)":[3],"can":[4,23,67],"be":[5],"employed":[6],"to":[7,30],"investigate":[8],"brain":[9,167],"activation":[10,168],"by":[11],"measuring":[12],"the":[13,61,64,71,80,153,155,158],"absorption":[14],"of":[15,85,88,103,157],"light":[17],"through":[18],"an":[19,136,171],"intact":[20],"skull.":[21],"fNIRS":[22,56,65,108],"measure":[24],"hemoglobin":[25],"signals,":[26],"which":[27,44],"are":[28,75,99],"similar":[29],"functional":[31],"magnetic":[32],"resonance":[33],"imaging":[34,57],"(fMRI)":[35],"blood-oxygen-level-dependent":[36],"(BOLD)":[37],"signals.":[38,109],"The":[39],"general":[40],"linear":[41],"model":[42],"(GLM),":[43],"is":[45],"a":[46,83,128,144,166],"standard":[47],"method":[48,133,160],"for":[49,55,107],"fMRI":[50],"imaging,":[51],"has":[52,82],"been":[53],"applied":[54],"analysis.":[58],"However,":[59,79,110],"when":[60,175],"subject":[62],"moves,":[63],"signal":[66],"contain":[68],"artifacts":[69,74],"during":[70],"measurement.":[72],"These":[73],"called":[76],"motion":[77,89,104,130],"artifacts.":[78,90],"GLM":[81],"drawback":[84],"failure":[86],"because":[87],"Recently,":[91],"wavelet":[92,145],"and":[93,143,170],"hemodynamic":[94],"response":[95],"function":[96],"based":[97,138,147],"algorithms":[98],"popular":[100],"detrending":[101,179],"methods":[102,112],"artifact":[105,131],"correction":[106,132],"these":[111],"cannot":[113],"show":[114],"impressive":[115],"performance":[116,156,173],"in":[117],"harsh":[118],"environments":[119],"such":[120],"as":[121],"overground":[122],"walking":[123],"tasks.":[124],"This":[125],"paper":[126],"suggests":[127],"new":[129],"that":[134],"uses":[135],"entropy":[137],"unbalanced":[139],"optode":[140],"decision":[141],"rule":[142],"regression":[146],"back":[148],"propagation":[149],"neural":[150],"network.":[151],"Through":[152],"experiments,":[154],"proposed":[159],"was":[161],"proven":[162],"using":[163],"graphic":[164],"results,":[165],"map,":[169],"objective":[172],"index":[174],"compared":[176],"with":[177],"conventional":[178],"algorithms.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}