{"id":"https://openalex.org/W2588748553","doi":"https://doi.org/10.1109/cisp-bmei.2016.7852943","title":"A new coherence estimating method: The magnitude squared coherence of smoothing minimum variance distortionless response","display_name":"A new coherence estimating method: The magnitude squared coherence of smoothing minimum variance distortionless response","publication_year":2016,"publication_date":"2016-10-01","ids":{"openalex":"https://openalex.org/W2588748553","doi":"https://doi.org/10.1109/cisp-bmei.2016.7852943","mag":"2588748553"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2016.7852943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2016.7852943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5013473628","display_name":"Dong Cui","orcid":"https://orcid.org/0000-0002-6468-1197"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Cui","raw_affiliation_strings":["School of Information Science and Engineering Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100347428","display_name":"Juan Wang","orcid":"https://orcid.org/0000-0003-3825-1609"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Juan Wang","raw_affiliation_strings":["School of Information Science and Engineering Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100376717","display_name":"Zhaohui Li","orcid":"https://orcid.org/0000-0003-4009-4374"},"institutions":[{"id":"https://openalex.org/I39333907","display_name":"Yanshan University","ror":"https://ror.org/02txfnf15","country_code":"CN","type":"education","lineage":["https://openalex.org/I39333907"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaohui Li","raw_affiliation_strings":["School of Information Science and Engineering Yanshan University, Qinhuangdao, China"],"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering Yanshan University, Qinhuangdao, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100418692","display_name":"Xiaoli Li","orcid":"https://orcid.org/0000-0003-1359-5130"},"institutions":[{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoli Li","raw_affiliation_strings":["Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China","institution_ids":["https://openalex.org/I25254941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013473628"],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.16788393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"48","issue":null,"first_page":"1440","last_page":"1445"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9990000128746033,"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":0.9959999918937683,"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/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.7308716177940369},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6767196655273438},{"id":"https://openalex.org/keywords/minimum-variance-unbiased-estimator","display_name":"Minimum-variance unbiased estimator","score":0.6739435791969299},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6365196704864502},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5799267292022705},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4819716811180115},{"id":"https://openalex.org/keywords/magnitude","display_name":"Magnitude (astronomy)","score":0.4811190664768219},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4132341146469116},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.41254401206970215},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35265102982521057},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1085309088230133}],"concepts":[{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.7308716177940369},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6767196655273438},{"id":"https://openalex.org/C165646398","wikidata":"https://www.wikidata.org/wiki/Q3755281","display_name":"Minimum-variance unbiased estimator","level":3,"score":0.6739435791969299},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6365196704864502},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5799267292022705},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4819716811180115},{"id":"https://openalex.org/C126691448","wikidata":"https://www.wikidata.org/wiki/Q2028919","display_name":"Magnitude (astronomy)","level":2,"score":0.4811190664768219},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4132341146469116},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.41254401206970215},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35265102982521057},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1085309088230133},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2016.7852943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2016.7852943","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1744075219","https://openalex.org/W1964230304","https://openalex.org/W1969125045","https://openalex.org/W1971992489","https://openalex.org/W1978153353","https://openalex.org/W1978736339","https://openalex.org/W2019377079","https://openalex.org/W2056669653","https://openalex.org/W2065460172","https://openalex.org/W2066218102","https://openalex.org/W2101928763","https://openalex.org/W2104006671","https://openalex.org/W2118240598","https://openalex.org/W2124040307","https://openalex.org/W2128945905","https://openalex.org/W2129413026","https://openalex.org/W2134353546","https://openalex.org/W2169749822","https://openalex.org/W2170391517","https://openalex.org/W2171392730","https://openalex.org/W6675238846"],"related_works":["https://openalex.org/W2376418092","https://openalex.org/W2072983018","https://openalex.org/W1016952678","https://openalex.org/W2356451205","https://openalex.org/W1990624027","https://openalex.org/W1513121561","https://openalex.org/W1967656023","https://openalex.org/W2141218268","https://openalex.org/W3089635587","https://openalex.org/W4242323026"],"abstract_inverted_index":{"The":[0,36,52,107],"magnitude":[1,88],"squared":[2,89],"coherence":[3,90],"(MSC)":[4],"is":[5,27,42,56,93],"an":[6],"important":[7],"method":[8,25,46,92],"to":[9],"calculate":[10],"the":[11,23,60,96,100,103,119],"connectivity":[12],"between":[13],"neural":[14],"signals.":[15],"It":[16],"provides":[17],"a":[18,43,63,86],"better":[19,116],"spectral":[20,44],"resolution":[21,76],"than":[22,118],"Welch's":[24],"and":[26,73,83],"often":[28],"used":[29],"in":[30,65,95],"analyzing":[31],"electroencephalograph":[32],"(EEG)":[33],"synchronization":[34],"activity.":[35],"minimum":[37],"variance":[38],"distortionless":[39],"response":[40],"(MVDR)":[41],"estimation":[45],"based":[47],"on":[48],"matched":[49],"filterbank":[50],"theory.":[51],"Cheriet-Belouchrani":[53],"(CB)":[54],"kernel":[55,105],"provided":[57],"for":[58],"measuring":[59],"energy":[61],"of":[62],"signal":[64],"time-frequency":[66],"distribution,":[67],"which":[68],"has":[69,115],"significant":[70],"interference":[71],"mitigation":[72],"preserves":[74],"high":[75],"measure":[77],"values.":[78],"By":[79],"combining":[80],"MVDR":[81,101,120],"spectra":[82],"CB":[84,104],"kernel,":[85],"new":[87],"estimating":[91],"proposed":[94],"paper":[97],"by":[98],"smoothing":[99],"with":[102],"(SMVDR).":[106],"simulation":[108],"results":[109],"show":[110],"that":[111],"SMVDR":[112],"MSC":[113,121],"approach":[114],"performances":[117],"method.":[122]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}