{"id":"https://openalex.org/W4412567250","doi":"https://doi.org/10.1109/access.2025.3591278","title":"Exploring Tensor-Based Optimization for Missing EEG Signal Recovery: A Comparative Study of Optimization Methods Across Different Tensor Decomposition Frameworks","display_name":"Exploring Tensor-Based Optimization for Missing EEG Signal Recovery: A Comparative Study of Optimization Methods Across Different Tensor Decomposition Frameworks","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412567250","doi":"https://doi.org/10.1109/access.2025.3591278"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3591278","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3591278","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3591278","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100333666","display_name":"Yue Zhang","orcid":"https://orcid.org/0000-0001-8825-4934"},"institutions":[{"id":"https://openalex.org/I87710204","display_name":"Beijing Sport University","ror":"https://ror.org/03w0k0x36","country_code":"CN","type":"education","lineage":["https://openalex.org/I87710204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhang","raw_affiliation_strings":["School of Sports Engineering, Beijing Sport University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Sports Engineering, Beijing Sport University, Beijing, China","institution_ids":["https://openalex.org/I87710204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112415171","display_name":"Hong Ge","orcid":"https://orcid.org/0009-0005-8843-5221"},"institutions":[{"id":"https://openalex.org/I87710204","display_name":"Beijing Sport University","ror":"https://ror.org/03w0k0x36","country_code":"CN","type":"education","lineage":["https://openalex.org/I87710204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huanmin Ge","raw_affiliation_strings":["School of Sports Engineering, Beijing Sport University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0005-8843-5221","affiliations":[{"raw_affiliation_string":"School of Sports Engineering, Beijing Sport University, Beijing, China","institution_ids":["https://openalex.org/I87710204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109437098","display_name":"Chencheng Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I87710204","display_name":"Beijing Sport University","ror":"https://ror.org/03w0k0x36","country_code":"CN","type":"education","lineage":["https://openalex.org/I87710204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chencheng Huang","raw_affiliation_strings":["School of Sports Engineering, Beijing Sport University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Sports Engineering, Beijing Sport University, Beijing, China","institution_ids":["https://openalex.org/I87710204"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053950348","display_name":"Xinhua Su","orcid":"https://orcid.org/0000-0002-9156-9336"},"institutions":[{"id":"https://openalex.org/I87710204","display_name":"Beijing Sport University","ror":"https://ror.org/03w0k0x36","country_code":"CN","type":"education","lineage":["https://openalex.org/I87710204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinhua Su","raw_affiliation_strings":["School of Sports Engineering, Beijing Sport University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Sports Engineering, Beijing Sport University, Beijing, China","institution_ids":["https://openalex.org/I87710204"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87710204"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.93,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.72560386,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"13","issue":null,"first_page":"130185","last_page":"130200"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9891999959945679,"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":0.9891999959945679,"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":0.945900022983551,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.9380999803543091,"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/tensor-decomposition","display_name":"Tensor decomposition","score":0.6562737226486206},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.6325244903564453},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6170647144317627},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5781505107879639},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"Electroencephalography","score":0.504609227180481},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.44328245520591736},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4429958164691925},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35422033071517944},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.34825366735458374},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29789960384368896},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27015310525894165},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.12339740991592407},{"id":"https://openalex.org/keywords/neuroscience","display_name":"Neuroscience","score":0.12260711193084717},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1088252067565918},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.0824114978313446}],"concepts":[{"id":"https://openalex.org/C2986737658","wikidata":"https://www.wikidata.org/wiki/Q30103009","display_name":"Tensor decomposition","level":3,"score":0.6562737226486206},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.6325244903564453},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6170647144317627},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5781505107879639},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.504609227180481},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.44328245520591736},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4429958164691925},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35422033071517944},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.34825366735458374},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29789960384368896},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27015310525894165},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.12339740991592407},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.12260711193084717},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1088252067565918},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0824114978313446},{"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3591278","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3591278","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e4932e3ba87f4fc4822b2cc73cec4819","is_oa":true,"landing_page_url":"https://doaj.org/article/e4932e3ba87f4fc4822b2cc73cec4819","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 130185-130200 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3591278","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3591278","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5397703342","display_name":null,"funder_award_id":"12371094","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6308768472","display_name":null,"funder_award_id":"2024JCYJ003","funder_id":"https://openalex.org/F4320334997","funder_display_name":"Chinese Universities Scientific Fund"},{"id":"https://openalex.org/G6793777365","display_name":null,"funder_award_id":"62301056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334997","display_name":"Chinese Universities Scientific Fund","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4379256054","https://openalex.org/W2093953080","https://openalex.org/W2911706637","https://openalex.org/W47805180","https://openalex.org/W3216281372","https://openalex.org/W2963838862","https://openalex.org/W2608089480","https://openalex.org/W3015641590","https://openalex.org/W2987657992","https://openalex.org/W4297666106"],"abstract_inverted_index":{"Electroencephalography":[0],"(EEG)":[1],"signals":[2,240],"are":[3],"frequently":[4],"compromised":[5],"by":[6,135,156],"missing":[7,41,72,105],"data":[8,106,164,213],"due":[9],"to":[10,71,192],"electrode":[11],"contact":[12],"issues":[13],"or":[14],"subject":[15],"movement.":[16],"Tensor":[17],"decomposition":[18,56],"has":[19],"emerged":[20],"as":[21],"a":[22,86,178],"powerful":[23],"technique":[24],"for":[25,36,237],"analyzing":[26],"multidimensional":[27],"EEG":[28,42,73,230,239],"data.":[29,74],"This":[30,232],"study":[31],"evaluates":[32],"various":[33],"tensor-based":[34],"methods":[35,46,202],"reconstructing":[37],"structured":[38,104],"and":[39,58,95,162,168,199,204,242],"unstructured":[40],"signals,":[43],"including":[44],"recovery":[45,70,93,132,152,223],"based":[47,129],"on":[48,130,153,159],"canonical":[49],"polyadic":[50],"(CP)":[51],"decomposition,":[52],"tensor":[53],"singular":[54],"value":[55],"(t-SVD),":[57],"tucker":[59],"decomposition.":[60],"Notably,":[61],"this":[62],"research":[63,233],"represents":[64],"the":[65,78,101,123,136,148,172,183,195,207,211,218],"first":[66],"application":[67],"of":[68,80,150,221],"t-SVD-based":[69,124,222],"To":[75],"rigorously":[76],"assess":[77],"efficacy":[79],"our":[81],"proposed":[82],"method,":[83,138],"we":[84],"implement":[85],"dual":[87],"evaluation":[88],"framework":[89],"encompassing:":[90],"a)":[91],"signal":[92,131,151],"indices":[94],"b)":[96],"classification":[97,154],"performance":[98,128,158],"metrics.":[99,170],"In":[100,171],"most":[102,173],"challenging":[103,246],"scenario":[107],"(missing":[108,190],"segments":[109],"<italic":[110,116],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[111,117,121],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">N</i>":[112],"=":[113,119],"80,":[114],"duration":[115],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">d</i>":[118],"1.8<italic":[120],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">s</i>),":[122],"method":[125,185,197,224],"demonstrate":[126,217],"superior":[127,219],"indices,":[133],"followed":[134],"tucker-based":[137,196],"while":[139],"CP-based":[140,201],"approaches":[141],"exhibited":[142],"inferior":[143],"results.":[144],"We":[145],"further":[146],"evaluate":[147],"impact":[149],"accuracy":[155,187,208],"comparing":[157],"complete,":[160],"missing,":[161],"recovered":[163],"using":[165],"six":[166],"classifiers":[167],"five":[169],"severe":[174],"structured-missing":[175],"case,":[176],"employing":[177],"Linear":[179],"Discriminant":[180],"Analysis":[181],"classifier,":[182],"t-SVD":[184],"enhance":[186],"from":[188],"46.36%":[189],"data)":[191],"56.50%,":[193],"surpassing":[194],"(54.29%)":[198],"both":[200],"(48.21%":[203],"49.07%),":[205],"approaching":[206],"achieved":[209],"with":[210],"original":[212],"(58.57%).":[214],"These":[215],"results":[216],"capability":[220],"in":[225,245],"preserving":[226],"discriminative":[227],"features":[228],"within":[229],"signals.":[231],"offers":[234],"promising":[235],"solutions":[236],"enhancing":[238],"quality":[241],"subsequent":[243],"analysis":[244],"real-world":[247],"scenarios.":[248]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-13T14:20:09.374765","created_date":"2025-10-10T00:00:00"}
