{"id":"https://openalex.org/W2790674571","doi":"https://doi.org/10.1109/camsap.2017.8313193","title":"Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment","display_name":"Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2790674571","doi":"https://doi.org/10.1109/camsap.2017.8313193","mag":"2790674571"},"language":"en","primary_location":{"id":"doi:10.1109/camsap.2017.8313193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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/A5055088251","display_name":"Kristina Naskovska","orcid":null},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Kristina Naskovska","raw_affiliation_strings":["Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany"],"affiliations":[{"raw_affiliation_string":"Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044110562","display_name":"Stephan Lau","orcid":"https://orcid.org/0000-0002-5952-0516"},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stephan Lau","raw_affiliation_strings":["Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005529040","display_name":"A. Prof. Amr Yehia Abou-Ghazala","orcid":null},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Amr Aboughazala","raw_affiliation_strings":["Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany"],"affiliations":[{"raw_affiliation_string":"Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004533757","display_name":"Martin Haardt","orcid":"https://orcid.org/0000-0001-7810-975X"},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Haardt","raw_affiliation_strings":["Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany"],"affiliations":[{"raw_affiliation_string":"Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044825834","display_name":"Jens Haueisen","orcid":"https://orcid.org/0000-0003-3871-2890"},"institutions":[{"id":"https://openalex.org/I119449181","display_name":"Technische Universit\u00e4t Ilmenau","ror":"https://ror.org/01weqhp73","country_code":"DE","type":"education","lineage":["https://openalex.org/I119449181"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jens Haueisen","raw_affiliation_strings":["Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany","institution_ids":["https://openalex.org/I119449181"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5055088251"],"corresponding_institution_ids":["https://openalex.org/I119449181"],"apc_list":null,"apc_paid":null,"fwci":0.2306,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.50159236,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9872000217437744,"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":0.9690999984741211,"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/magnetoencephalography","display_name":"Magnetoencephalography","score":0.819893479347229},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.6446365714073181},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.6160497069358826},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.5565478801727295},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5561509728431702},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5483170747756958},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5220686793327332},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5012590885162354},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.452178955078125},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4417071044445038},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.41614121198654175},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3973177969455719},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3151768445968628},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21333974599838257},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.11290639638900757},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10217785835266113},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.10102710127830505},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09208130836486816}],"concepts":[{"id":"https://openalex.org/C556910895","wikidata":"https://www.wikidata.org/wiki/Q384188","display_name":"Magnetoencephalography","level":3,"score":0.819893479347229},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.6446365714073181},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.6160497069358826},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.5565478801727295},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5561509728431702},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5483170747756958},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5220686793327332},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5012590885162354},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.452178955078125},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4417071044445038},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.41614121198654175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3973177969455719},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3151768445968628},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21333974599838257},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11290639638900757},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10217785835266113},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.10102710127830505},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09208130836486816},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"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},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/camsap.2017.8313193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/camsap.2017.8313193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","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":16,"referenced_works":["https://openalex.org/W1480059945","https://openalex.org/W1605513324","https://openalex.org/W1632866817","https://openalex.org/W1963711906","https://openalex.org/W1975376466","https://openalex.org/W2024165284","https://openalex.org/W2033845178","https://openalex.org/W2072640712","https://openalex.org/W2119412403","https://openalex.org/W2329696737","https://openalex.org/W2405410592","https://openalex.org/W2562604834","https://openalex.org/W2592506141","https://openalex.org/W2765959000","https://openalex.org/W6635960866","https://openalex.org/W6636705969"],"related_works":["https://openalex.org/W2950186459","https://openalex.org/W2170114491","https://openalex.org/W2897298721","https://openalex.org/W2242624680","https://openalex.org/W2136127937","https://openalex.org/W4290987221","https://openalex.org/W2216309014","https://openalex.org/W2569661359","https://openalex.org/W3199841771","https://openalex.org/W4286971555"],"abstract_inverted_index":{"Simultaneously":[0],"recorded":[1,68],"magnetoencephalography":[2],"(MEG)":[3],"and":[4,70,76,107,133,149,153,174,201],"electroencephalography":[5],"(EEG)":[6],"signals":[7,72,109,214,225],"can":[8],"benefit":[9],"from":[10],"a":[11,90,111,119,127,216,232],"joint":[12],"analysis":[13,144],"based":[14],"on":[15,177],"coupled":[16,23,142,157,244],"Canonical":[17],"Polyadic":[18],"(CP)":[19],"tensor":[20,100],"decompositions.":[21],"The":[22,38,102,122,141,183,210,228],"CP":[24,45],"decomposition":[25,46,158],"jointly":[26],"decomposes":[27],"tensors":[28],"that":[29,115],"have":[30],"at":[31,137],"least":[32],"one":[33],"factor":[34,56],"matrix":[35,49],"in":[36,84,98,240],"common.":[37],"Coupled":[39],"-":[40],"Semi-Algebraic":[41],"framework":[42,51,230],"for":[43,147,168,236],"approximate":[44],"via":[47],"SImultaneous":[48],"diagonalization":[50],"(C-SECSI)":[52],"efficiently":[53],"estimates":[54],"the":[55,94,99,105,130,163,175,189,196,203,207,222],"matrices":[57],"with":[58,186,243],"adjustable":[59],"complexity-accuracy":[60,190],"trade-offs.":[61],"Our":[62],"objective":[63],"is":[64,96,110,116,124,145,231],"to":[65,86,188],"decompose":[66],"simultaneously":[67],"MEG":[69,106,148,213],"EEG":[71,108,150,224],"above":[73,77],"intact":[74,211],"skull":[75,80,131,212],"two":[78],"conducting":[79],"defects":[81],"using":[82],"C-SECSI":[83,229],"order":[85],"determine":[87],"how":[88],"such":[89,246],"tissue":[91],"anomaly":[92],"of":[93,104,180,192],"head":[95],"reflected":[97],"rank.":[101],"source":[103,169],"miniaturized":[112],"electric":[113],"dipole":[114,123],"implanted":[117],"into":[118],"rabbit's":[120],"brain.":[121],"shifted":[125],"along":[126],"line":[128],"under":[129,171],"defects,":[132],"measurements":[134],"are":[135],"taken":[136],"regularly":[138],"spaced":[139],"points.":[140],"SECSI":[143],"conducted":[146],"measurement":[151],"series":[152],"ranks":[154],"1-3.":[155],"This":[156],"produces":[159],"meaningful":[160],"components":[161],"representing":[162],"three":[164,197],"characteristic":[165,198],"signal":[166,238],"topographies":[167],"positions":[170,176],"defect":[172,181],"1":[173],"either":[178],"side":[179],"1.":[182],"rank":[184,193],"estimation":[185],"respect":[187],"trade-off":[191],"3":[194],"reflects":[195],"cases":[199],"well":[200],"matches":[202],"dimensions":[204],"spanned":[205],"by":[206],"data":[208,242],"set.":[209],"show":[215],"higher":[217],"complexity":[218],"(rank":[219,226],"3)":[220],"than":[221],"corresponding":[223],"1).":[227],"very":[233],"promising":[234],"method":[235],"blind":[237],"separation":[239],"multidimensional":[241],"modalities,":[245],"as":[247],"simultaneous":[248],"MEG-EEG.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
