{"id":"https://openalex.org/W2119902872","doi":"https://doi.org/10.1109/tsp.2005.853209","title":"Regularized spectral matching for blind source separation. Application to fMRI imaging","display_name":"Regularized spectral matching for blind source separation. Application to fMRI imaging","publication_year":2005,"publication_date":"2005-08-16","ids":{"openalex":"https://openalex.org/W2119902872","doi":"https://doi.org/10.1109/tsp.2005.853209","mag":"2119902872"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2005.853209","doi":"https://doi.org/10.1109/tsp.2005.853209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2005.853209","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-a","jufo-3","ki-jl-1","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"volume":null,"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/A5066400774","display_name":"Hichem Snoussi","orcid":"https://orcid.org/0000-0002-6563-2135","observed_orcids":["https://orcid.org/0000-0002-6563-2135"]},"institutions":[{"id":"https://openalex.org/I140494188","display_name":"Universit\u00e9 de Technologie de Troyes","ror":"https://ror.org/01qhqcj41","country_code":"FR","type":"education","lineage":["https://openalex.org/I140494188"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"H. Snoussi","raw_affiliation_strings":["M2S/ISTIT Laboratory, University of Technology of Troyes, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"M2S/ISTIT Laboratory, University of Technology of Troyes, France","institution_ids":["https://openalex.org/I140494188"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032850756","display_name":"Vince D. Calhoun","orcid":"https://orcid.org/0000-0001-9058-0747","observed_orcids":["https://orcid.org/0000-0001-9058-0747"]},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"V.D. Calhoun","raw_affiliation_strings":["Institute of Living, Olin Neuropsychiatry Research Center, Hartford, CT, USA","Yale University, New Heaven, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Living, Olin Neuropsychiatry Research Center, Hartford, CT, USA","institution_ids":[]},{"raw_affiliation_string":"Yale University, New Heaven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":2645,"currency":"USD","value_usd":2645},"apc_paid":null,"fwci":0.4776,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.76919279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"53","issue":"9","first_page":"3373","last_page":"3383"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/T12303","display_name":"Tensor decomposition and applications","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.0,"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/blind-source-separation","display_name":"blind source separation","score":0.9869999885559082},{"id":"https://openalex.org/keywords/spectral-matching","display_name":"spectral matching","score":0.7620000243186951},{"id":"https://openalex.org/keywords/time-varying-spectrum","display_name":"time-varying spectrum","score":0.6940000057220459},{"id":"https://openalex.org/keywords/second-order-statistics","display_name":"second-order statistics","score":0.8040000200271606},{"id":"https://openalex.org/keywords/short-time-fourier-transform","display_name":"short-time Fourier transform","score":0.8420000076293945},{"id":"https://openalex.org/keywords/time-frequency-domain","display_name":"time-frequency domain","score":0.6819999814033508},{"id":"https://openalex.org/keywords/expectation-maximization-algorithm","display_name":"expectation-maximization algorithm","score":0.7770000100135803},{"id":"https://openalex.org/keywords/bayesian-estimation","display_name":"Bayesian estimation","score":0.7200000286102295},{"id":"https://openalex.org/keywords/fmri","display_name":"functional MRI","score":0.5659999847412109},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"noise reduction","score":0.49399998784065247},{"id":"https://openalex.org/keywords/artifact-removal","display_name":"artifact removal","score":0.47999998927116394},{"id":"https://openalex.org/keywords/block-designs","display_name":"block designs","score":0.4449999928474426},{"id":"https://openalex.org/keywords/visual-stimuli","display_name":"visual stimuli","score":0.5960000157356262}],"concepts":[{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.6779131293296814},{"id":"https://openalex.org/C166386157","wikidata":"https://www.wikidata.org/wiki/Q1477735","display_name":"Short-time Fourier transform","level":4,"score":0.6515161991119385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6112614274024963},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5585875511169434},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.5386602282524109},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5104274153709412},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.502117395401001},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47245553135871887},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.4269866943359375},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4208317995071411},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.40878257155418396},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3590880036354065},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3570725917816162},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2169542908668518},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16508010029792786},{"id":"https://openalex.org/C203024314","wikidata":"https://www.wikidata.org/wiki/Q1365258","display_name":"Fourier analysis","level":3,"score":0.15263453125953674},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.1032913327217102},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2005.853209","doi":"https://doi.org/10.1109/tsp.2005.853209","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2005.853209","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-scientific","cwts-core","dongbi-a","jufo-3","ki-jl-1","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-02359105v1","doi":null,"is_oa":false,"landing_page_url":"https://utt.hal.science/hal-02359105","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4405252976","host_organization_name":"Centre pour la Communication Scientifique Directe","host_organization_lineage":["https://openalex.org/I4405252976"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Signal Processing, 2005, 53 (9), pp.3373-3383. &#x27E8;10.1109/TSP.2005.853209&#x27E9;","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W271314235","https://openalex.org/W1501281527","https://openalex.org/W1514159701","https://openalex.org/W1548802052","https://openalex.org/W1573082642","https://openalex.org/W1582948285","https://openalex.org/W1620339291","https://openalex.org/W1625780261","https://openalex.org/W1851993003","https://openalex.org/W1996355918","https://openalex.org/W1997652134","https://openalex.org/W2016444985","https://openalex.org/W2019502123","https://openalex.org/W2038948755","https://openalex.org/W2049633694","https://openalex.org/W2055439724","https://openalex.org/W2081771698","https://openalex.org/W2097242476","https://openalex.org/W2099741732","https://openalex.org/W2101303708","https://openalex.org/W2106219595","https://openalex.org/W2106706098","https://openalex.org/W2108384452","https://openalex.org/W2115687448","https://openalex.org/W2123965708","https://openalex.org/W2124195644","https://openalex.org/W2124757684","https://openalex.org/W2133436097","https://openalex.org/W2137969290","https://openalex.org/W2142638745","https://openalex.org/W2950250097","https://openalex.org/W4211083646","https://openalex.org/W6670892466"],"related_works":["https://openalex.org/W2167610435","https://openalex.org/W1982476701","https://openalex.org/W4379086693","https://openalex.org/W1534062893","https://openalex.org/W4414110653","https://openalex.org/W1578097886","https://openalex.org/W283503318","https://openalex.org/W3203590456","https://openalex.org/W2184531506","https://openalex.org/W2545802437"],"abstract_inverted_index":{"The":[0,29,85,98],"main":[1],"contribution":[2],"of":[3,25,31,39,82,88,132],"this":[4],"paper":[5],"is":[6,91,100],"to":[7,144],"present":[8],"a":[9,61,69,103,114],"Bayesian":[10],"approach":[11],"for":[12],"solving":[13],"the":[14,26,32,37,40,48,52,58,78,83,89,121,124,126,130,140],"noisy":[15],"instantaneous":[16],"blind":[17,33],"source":[18],"separation":[19],"problem":[20],"based":[21],"on":[22,36,102],"second-order":[23,41],"statistics":[24,42],"time-varying":[27,79],"spectrum.":[28],"success":[30],"estimation":[34,86],"relies":[35],"nonstationarity":[38],"and":[43,56,138],"their":[44],"intersource":[45],"diversity.":[46],"Choosing":[47],"time-frequency":[49,96],"domain":[50],"as":[51],"signal":[53],"representation":[54],"space":[55],"transforming":[57],"data":[59],"by":[60,93],"short-time":[62],"Fourier":[63],"transform":[64],"(STFT),":[65],"our":[66],"method":[67],"presents":[68],"simple":[70],"EM":[71],"algorithm":[72,99,128],"that":[73],"can":[74],"efficiently":[75],"deal":[76],"with":[77],"spectrum":[80],"diversity":[81],"sources.":[84],"variance":[87],"STFT":[90],"reduced":[92],"averaging":[94],"across":[95],"subdomains.":[97],"demonstrated":[101],"standard":[104],"functional":[105],"resonance":[106],"imaging":[107],"(fMRI)":[108],"experiment":[109],"involving":[110],"visual":[111],"stimuli":[112],"in":[113,123],"block":[115],"design.":[116],"Explicitly":[117],"taking":[118],"into":[119],"account":[120],"noise":[122],"model,":[125],"proposed":[127],"has":[129],"advantage":[131],"extracting":[133],"only":[134],"relevant":[135],"task-related":[136],"components":[137,142],"considers":[139],"remaining":[141],"(artifacts)":[143],"be":[145],"noise.":[146]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-25T09:29:13.331239","created_date":"2016-06-24T00:00:00"}
