{"id":"https://openalex.org/W2979840005","doi":"https://doi.org/10.1109/embc.2019.8857687","title":"Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels","display_name":"Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2979840005","doi":"https://doi.org/10.1109/embc.2019.8857687","mag":"2979840005","pmid":"https://pubmed.ncbi.nlm.nih.gov/31946224"},"language":"en","primary_location":{"id":"doi:10.1109/embc.2019.8857687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5113457621","display_name":"Guofa Shou","orcid":null},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guofa Shou","raw_affiliation_strings":["Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73072, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73072, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101654842","display_name":"Lei Ding","orcid":"https://orcid.org/0000-0003-2277-267X"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Ding","raw_affiliation_strings":["Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73072, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, 73072, USA","institution_ids":["https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I8692664"],"apc_list":null,"apc_paid":null,"fwci":0.1246,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.49330175,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"2019","issue":null,"first_page":"1698","last_page":"1701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10241","display_name":"Functional Brain Connectivity Studies","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/T10241","display_name":"Functional Brain Connectivity Studies","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/T10581","display_name":"Neural dynamics and brain function","score":0.9997000098228455,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9977999925613403,"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/independent-component-analysis","display_name":"Independent component analysis","score":0.825400710105896},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.7088404297828674},{"id":"https://openalex.org/keywords/magnetoencephalography","display_name":"Magnetoencephalography","score":0.6736853718757629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6403945088386536},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6365764141082764},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.6024896502494812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5881999731063843},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5784440040588379},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.5528464317321777},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.531179666519165},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5064777135848999},{"id":"https://openalex.org/keywords/brain-activity-and-meditation","display_name":"Brain activity and meditation","score":0.48205670714378357},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.45721080899238586},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.44368046522140503},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.43809065222740173},{"id":"https://openalex.org/keywords/pearson-product-moment-correlation-coefficient","display_name":"Pearson product-moment correlation coefficient","score":0.4278738498687744},{"id":"https://openalex.org/keywords/resting-state-fmri","display_name":"Resting state fMRI","score":0.41887518763542175},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.41337862610816956},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2571561336517334},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2543978691101074},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24046391248703003},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.13595271110534668},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12727713584899902}],"concepts":[{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.825400710105896},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.7088404297828674},{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.6736853718757629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6403945088386536},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6365764141082764},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.6024896502494812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5881999731063843},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5784440040588379},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.5528464317321777},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.531179666519165},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5064777135848999},{"id":"https://openalex.org/C120843803","wikidata":"https://www.wikidata.org/wiki/Q4955807","display_name":"Brain activity and meditation","level":3,"score":0.48205670714378357},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.45721080899238586},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.44368046522140503},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.43809065222740173},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.4278738498687744},{"id":"https://openalex.org/C66324658","wikidata":"https://www.wikidata.org/wiki/Q7316120","display_name":"Resting state fMRI","level":2,"score":0.41887518763542175},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.41337862610816956},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2571561336517334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2543978691101074},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24046391248703003},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.13595271110534668},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12727713584899902},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001921","descriptor_name":"Brain","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001931","descriptor_name":"Brain Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001931","descriptor_name":"Brain Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001931","descriptor_name":"Brain Mapping","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D004569","descriptor_name":"Electroencephalography","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008279","descriptor_name":"Magnetic Resonance Imaging","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012044","descriptor_name":"Regression Analysis","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1109/embc.2019.8857687","is_oa":false,"landing_page_url":"https://doi.org/10.1109/embc.2019.8857687","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","raw_type":"proceedings-article"},{"id":"pmid:31946224","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31946224","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4000000059604645,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1968697559","https://openalex.org/W1970695058","https://openalex.org/W1973042443","https://openalex.org/W1980447390","https://openalex.org/W1985327120","https://openalex.org/W2009494091","https://openalex.org/W2012423033","https://openalex.org/W2022607432","https://openalex.org/W2023869733","https://openalex.org/W2027781650","https://openalex.org/W2063294564","https://openalex.org/W2079450984","https://openalex.org/W2079537726","https://openalex.org/W2081398181","https://openalex.org/W2095064393","https://openalex.org/W2128495200","https://openalex.org/W2137526583","https://openalex.org/W2143112732","https://openalex.org/W2155658404","https://openalex.org/W2168036349","https://openalex.org/W2255433468","https://openalex.org/W2593796352","https://openalex.org/W2618005610","https://openalex.org/W2790994475","https://openalex.org/W2802342292","https://openalex.org/W4241074797"],"related_works":["https://openalex.org/W2090251673","https://openalex.org/W2060698208","https://openalex.org/W2259241868","https://openalex.org/W2417900385","https://openalex.org/W2295974754","https://openalex.org/W2367562875","https://openalex.org/W2905582910","https://openalex.org/W2548851361","https://openalex.org/W3213413644","https://openalex.org/W2585496609"],"abstract_inverted_index":{"Intrinsic":[0],"connectivity":[1],"networks":[2],"(ICNs)":[3],"have":[4,301],"been":[5],"widely":[6,38],"studied":[7],"using":[8,210],"functional":[9],"magnetic":[10],"resonance":[11],"imaging":[12],"(fMRI)":[13],"data":[14,17],"and":[15,32,73,98,105,121,198,217],"electrophysiological":[16],"(e.g.,":[18],"electroencephalography":[19],"(EEG)":[20],"or":[21],"magnetoencephalography":[22],"(MEG)).":[23],"Two":[24],"major":[25,175],"methods,":[26],"i.e.,":[27],"seed-based":[28],"correlation":[29,75,106,197,230],"analysis":[30,35,51,100,129,213,280],"(SBCA)":[31],"independent":[33],"component":[34],"(ICA),":[36],"are":[37],"used":[39,293],"to":[40,54,77,162,174],"extract":[41,78],"ICNs.":[42,60,148,267],"Among":[43],"them,":[44],"ICA":[45,97,104],"usually":[46],"involves":[47],"a":[48,64],"dual":[49],"regression":[50,99,125,165,199,212,279],"in":[52,115,126,145,178,222,283,294],"order":[53],"obtain":[55],"final":[56],"spatial":[57,189,220,248,263],"definitions":[58],"of":[59,102,112,124,141,155,191,235,250,265,298],"Recently,":[61],"we":[62,89,136,150],"proposed":[63,90,143,185,275],"framework":[65,93,276],"that":[66,94,170,224,273],"includes":[67],"cortical":[68,79,147,171,192,207,226,251,266,285,306],"source":[69],"imaging,":[70],"source-level":[71,103],"ICA,":[72],"statistical":[74],"analysis,":[76,107],"ICNs":[80,172,176,193,208,227,286],"from":[81,119,228,287],"resting-state":[82],"EEG":[83],"data.":[84],"In":[85,132,187,268],"the":[86,109,122,133,139,142,156,164,184,206,211,225,229,238,254,274,278,290,295],"present":[87,134],"study,":[88,135],"an":[91],"alternative":[92],"uses":[95],"sensor-level":[96,113],"instead":[101],"considering":[108],"well-studied":[110],"characteristics":[111],"ICs":[114],"differentiating":[116],"neural":[117],"activities":[118],"artifacts":[120],"benefit":[123],"accommodating":[127],"multivariate":[128],"over":[130],"correlation.":[131],"mainly":[137],"investigated":[138,152],"performance":[140],"procedure":[144],"extracting":[146],"Meanwhile,":[149],"also":[151],"different":[153,160,233,255],"variants":[154,234],"regressors":[157,236,299],"sampled":[158],"at":[159,237],"frequencies":[161,257],"formulate":[163],"model.":[166],"The":[167,232],"results":[168],"demonstrated":[169],"corresponding":[173],"identified":[177],"literature":[179],"could":[180],"be":[181],"obtained":[182,194],"by":[183],"framework.":[186],"general,":[188,223],"patterns":[190,249,264],"via":[195],"both":[196],"analyses":[200],"show":[201,258],"statistically":[202],"significant":[203],"similarity.":[204],"However,":[205],"reconstructed":[209,305],"exhibit":[214],"more":[215,218],"focal":[216],"superficial":[219],"patterns,":[221],"analysis.":[231],"same":[239],"sampling":[240,256,291],"frequency":[241,292],"do":[242],"not":[243],"produce":[244],"obvious":[245],"impacts":[246,303],"on":[247,261,304],"ICNs,":[252],"while":[253,289],"large":[259,302],"effects":[260],"extracted":[262],"summary,":[269],"it":[270],"is":[271,281],"suggested":[272],"with":[277],"promising":[282],"reconstructing":[284],"EEG,":[288],"formulation":[296],"process":[297],"may":[300],"ICN":[307],"patterns.":[308]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2019-10-18T00:00:00"}
