{"id":"https://openalex.org/W3019624036","doi":"https://doi.org/10.1109/civemsa45640.2019.9071605","title":"Functional Corticomuscular Coupling Based on Bivariate Empirical Mode Decomposition - Multiscale Transfer Entropy","display_name":"Functional Corticomuscular Coupling Based on Bivariate Empirical Mode Decomposition - Multiscale Transfer Entropy","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W3019624036","doi":"https://doi.org/10.1109/civemsa45640.2019.9071605","mag":"3019624036"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa45640.2019.9071605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071605","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5026389273","display_name":"Shengcui Cheng","orcid":"https://orcid.org/0000-0001-6581-6981"},"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":"Shengcui Cheng","raw_affiliation_strings":["Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408265","display_name":"Xiaoling Chen","orcid":"https://orcid.org/0000-0003-3677-3753"},"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":"Xiaoling Chen","raw_affiliation_strings":["Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061377407","display_name":"Ping Xie","orcid":"https://orcid.org/0000-0001-5878-087X"},"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":"Ping Xie","raw_affiliation_strings":["Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036702950","display_name":"Xiaohui Pang","orcid":null},"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":"Xiaohui Pang","raw_affiliation_strings":["Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China","institution_ids":["https://openalex.org/I39333907"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067531329","display_name":"Xiaolin Bai","orcid":"https://orcid.org/0000-0001-5044-509X"},"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":"Xiaolin Bai","raw_affiliation_strings":["Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China"],"affiliations":[{"raw_affiliation_string":"Key Lab of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei, China","institution_ids":["https://openalex.org/I39333907"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5026389273"],"corresponding_institution_ids":["https://openalex.org/I39333907"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.2166475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9958000183105469,"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":0.9958000183105469,"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/T10581","display_name":"Neural dynamics and brain function","score":0.994700014591217,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.9936000108718872,"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/hilbert\u2013huang-transform","display_name":"Hilbert\u2013Huang transform","score":0.7267855405807495},{"id":"https://openalex.org/keywords/bivariate-analysis","display_name":"Bivariate analysis","score":0.6743638515472412},{"id":"https://openalex.org/keywords/transfer-entropy","display_name":"Transfer entropy","score":0.6199092864990234},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6008374094963074},{"id":"https://openalex.org/keywords/coupling-strength","display_name":"Coupling strength","score":0.4979889392852783},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4565470218658447},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.447157084941864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39127397537231445},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3449423611164093},{"id":"https://openalex.org/keywords/biological-system","display_name":"Biological system","score":0.34072744846343994},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.29286304116249084},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.18382102251052856},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.14817127585411072},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.13016819953918457},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.10816776752471924},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.10638836026191711}],"concepts":[{"id":"https://openalex.org/C25570617","wikidata":"https://www.wikidata.org/wiki/Q1006462","display_name":"Hilbert\u2013Huang transform","level":3,"score":0.7267855405807495},{"id":"https://openalex.org/C64341305","wikidata":"https://www.wikidata.org/wiki/Q4919225","display_name":"Bivariate analysis","level":2,"score":0.6743638515472412},{"id":"https://openalex.org/C182049051","wikidata":"https://www.wikidata.org/wiki/Q17147155","display_name":"Transfer entropy","level":3,"score":0.6199092864990234},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6008374094963074},{"id":"https://openalex.org/C2986219828","wikidata":"https://www.wikidata.org/wiki/Q1193095","display_name":"Coupling strength","level":2,"score":0.4979889392852783},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4565470218658447},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.447157084941864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39127397537231445},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3449423611164093},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.34072744846343994},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.29286304116249084},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.18382102251052856},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.14817127585411072},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.13016819953918457},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10816776752471924},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.10638836026191711},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa45640.2019.9071605","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071605","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1994251555","https://openalex.org/W2000982976","https://openalex.org/W2006733010","https://openalex.org/W2035479505","https://openalex.org/W2059851411","https://openalex.org/W2077883484","https://openalex.org/W2092197838","https://openalex.org/W2117787517","https://openalex.org/W2150331341","https://openalex.org/W2171070794","https://openalex.org/W2801042373","https://openalex.org/W3014867361"],"related_works":["https://openalex.org/W2060031264","https://openalex.org/W2792603123","https://openalex.org/W2038532001","https://openalex.org/W2082036218","https://openalex.org/W3020215857","https://openalex.org/W3025804075","https://openalex.org/W1921775362","https://openalex.org/W1992744663","https://openalex.org/W2068221248","https://openalex.org/W2959442939","https://openalex.org/W2966844138","https://openalex.org/W3206895711","https://openalex.org/W2921643835","https://openalex.org/W2938008639","https://openalex.org/W3134363077","https://openalex.org/W2754022881","https://openalex.org/W2125082654","https://openalex.org/W2979837835","https://openalex.org/W1971439635","https://openalex.org/W2152376362"],"abstract_inverted_index":{"The":[0,25,134,210],"functional":[1],"corticomuscular":[2],"coupling":[3,72,120,162,194],"(FCMC)":[4],"is":[5,140,203],"a":[6,105],"physiological":[7],"phenomenon":[8],"to":[9,45,74,79,103,116],"reflect":[10],"the":[11,15,37,47,51,56,65,71,81,84,94,101,118,127,138,160,174,180,188,193,207,214,220,228,232,242,246],"multilayered":[12,26],"characteristics":[13,27,69,226],"of":[14,42,50,70],"information":[16],"interaction":[17],"between":[18,36,121,227],"electroencephalogram":[19],"(EEG)":[20],"and":[21,34,39,67,123,145,152,179,224,231],"electromyographic":[22],"(EMG)":[23],"signals.":[24],"such":[28],"as":[29],"local":[30,85,128,221],"frequency":[31,86,129,222],"band,":[32],"complex":[33,68],"multiscale":[35,57,66,225],"brain":[38],"muscles":[40],"are":[41],"great":[43],"significance":[44],"understand":[46],"cooperative":[48],"function":[49],"motor-sensory":[52],"neural":[53],"network.":[54],"Though":[55],"transfer":[58,113],"entropy":[59,114],"(MSTE)":[60],"method":[61,216],"can":[62,217],"effectively":[63],"describe":[64,80,219],"signals":[73,125],"some":[75],"extent,":[76],"it":[77],"fails":[78],"FCMC":[82,139],"on":[83,126,168,173,187,245],"band.":[87],"Therefore,":[88],"in":[89,142,199,206,235],"this":[90],"study,":[91],"we":[92],"combined":[93],"bivariate":[95,109],"empirical":[96,110],"mode":[97,111],"decomposition":[98],"(BEMD)":[99],"with":[100],"MSTE":[102],"construct":[104],"new":[106],"model,":[107],"named":[108],"decomposition-multiscale":[112],"(BMTE),":[115],"quantify":[117],"synchronous":[119],"EEG":[122,148],"EMG":[124,146,201],"band":[130,167,185,198,223],"at":[131,150,165,183,196],"different":[132,169],"scales.":[133],"results":[135,211],"show":[136,212],"that":[137,213],"significant":[141,181,205],"both":[143],"EEG\u2192EMG":[144],"\u2192":[147],"directions":[149],"betal":[151,184],"beta2":[153,166],"bands":[154],"during":[155],"steady-state":[156],"grip":[157],"task.":[158],"Meanwhile,":[159],"maximum":[161],"strength":[163,195],"value":[164,182],"scales":[170,176],"alomost":[171],"occur":[172],"high":[175],"(9-16":[177],"scales),":[178],"was":[186],"lower":[189],"time":[190],"scale.":[191,209],"Additionally,":[192],"gamma":[197],"EEG\u2192":[200],"direction":[202],"also":[204],"higher":[208],"BMTE":[215],"quantitatively":[218],"motor":[229,236],"cortex":[230],"contralateral":[233],"muscle":[234],"control":[237],"system.":[238],"This":[239],"study":[240],"extends":[241],"relative":[243],"researches":[244],"FCMC.":[247]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
