{"id":"https://openalex.org/W2692303295","doi":"https://doi.org/10.1109/icassp.2017.7952329","title":"Extraction of common task signals and spatial maps from group fMRI using a PARAFAC-based tensor decomposition technique","display_name":"Extraction of common task signals and spatial maps from group fMRI using a PARAFAC-based tensor decomposition technique","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2692303295","doi":"https://doi.org/10.1109/icassp.2017.7952329","mag":"2692303295"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952329","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 Acoustics, Speech and Signal Processing (ICASSP)","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/A5006679502","display_name":"Bhaskar Sen","orcid":"https://orcid.org/0000-0002-4548-5625"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bhaskar Sen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007884053","display_name":"Keshab K. Parhi","orcid":"https://orcid.org/0000-0001-6543-2793"},"institutions":[{"id":"https://openalex.org/I130238516","display_name":"University of Minnesota","ror":"https://ror.org/017zqws13","country_code":"US","type":"education","lineage":["https://openalex.org/I130238516"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Keshab K. Parhi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Minnesota, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Minnesota, USA","institution_ids":["https://openalex.org/I130238516"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5006679502"],"corresponding_institution_ids":["https://openalex.org/I130238516"],"apc_list":null,"apc_paid":null,"fwci":1.1531,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.76273885,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1113","last_page":"1117"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998000264167786,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9987000226974487,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.7548298835754395},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6816851496696472},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6327270269393921},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5345517992973328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5215629935264587},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.4947368800640106},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.47040802240371704},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4676627218723297},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.45532041788101196},{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.426530122756958},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4121389389038086},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09594497084617615}],"concepts":[{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.7548298835754395},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6816851496696472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6327270269393921},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5345517992973328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5215629935264587},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.4947368800640106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.47040802240371704},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4676627218723297},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.45532041788101196},{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.426530122756958},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4121389389038086},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09594497084617615},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","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},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952329","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 Acoustics, Speech and Signal Processing (ICASSP)","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":22,"referenced_works":["https://openalex.org/W1963826206","https://openalex.org/W1969060127","https://openalex.org/W1985327120","https://openalex.org/W1998039677","https://openalex.org/W2001789423","https://openalex.org/W2002562224","https://openalex.org/W2013115634","https://openalex.org/W2015408160","https://openalex.org/W2019219605","https://openalex.org/W2024729467","https://openalex.org/W2075665712","https://openalex.org/W2103386389","https://openalex.org/W2114973222","https://openalex.org/W2119741678","https://openalex.org/W2128495200","https://openalex.org/W2158997913","https://openalex.org/W2170668800","https://openalex.org/W2333836441","https://openalex.org/W2469230926","https://openalex.org/W2520786165","https://openalex.org/W2533647335","https://openalex.org/W3099152244"],"related_works":["https://openalex.org/W2390344110","https://openalex.org/W2046761971","https://openalex.org/W2364896863","https://openalex.org/W2361066326","https://openalex.org/W2182042810","https://openalex.org/W2156932837","https://openalex.org/W2380698615","https://openalex.org/W374502268","https://openalex.org/W2103029460","https://openalex.org/W1785857632"],"abstract_inverted_index":{"Blind":[0],"source":[1],"separation":[2],"(BSS)":[3],"using":[4,75],"independent":[5],"component":[6],"based":[7],"analysis":[8],"(e.g.,":[9],"probabilistic":[10],"ICA":[11],"and":[12,92],"infomax":[13],"ICA)":[14],"have":[15,108],"been":[16],"studied":[17],"in-depth":[18],"to":[19,61,119],"extract":[20,87,120],"common":[21,67,89,121],"hemodynamic":[22,106,114],"sources":[23,40],"for":[24,68,123,146],"a":[25,76,96,124],"group":[26,97,125],"of":[27,46,65,98,126],"functional":[28],"magnetic":[29],"resonance":[30],"images":[31],"(fMRI).":[32],"The":[33,104,131],"inherent":[34],"assumption":[35],"here":[36],"is":[37,53,57,128,141],"that":[38,74],"the":[39,47,51,63,69,88,135,142],"must":[41],"be":[42],"non-Gaussian.":[43],"For":[44],"most":[45,143],"real":[48],"world":[49],"data,":[50],"decomposition":[52,133,150],"non-unique.":[54],"Furthermore,":[55],"there":[56],"no":[58],"quantitative":[59,117],"way":[60],"determine":[62],"component(s)":[64],"interest":[66],"group.":[70],"This":[71],"paper":[72],"shows":[73],"novel":[77],"constrained":[78],"Parallel":[79],"Factor":[80],"Analysis":[81],"(PARAFAC)-based":[82],"tensor":[83,149],"decomposition,":[84],"one":[85],"can":[86],"task":[90],"signals":[91,107],"spatial":[93],"maps":[94],"from":[95],"noisy":[99],"fMRI":[100],"as":[101],"rank-1":[102],"tensors.":[103],"extracted":[105],"very":[109],"high":[110],"correlation":[111],"with":[112],"ideal":[113],"response.":[115],"A":[116],"algorithm":[118],"components":[122],"subjects":[127],"also":[129],"presented.":[130],"modified":[132],"preserves":[134],"uniqueness":[136],"under":[137],"mild":[138],"conditions":[139],"which":[140],"attractive":[144],"feature":[145],"any":[147],"PARAFAC-based":[148],"approach.":[151]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
