{"id":"https://openalex.org/W7222923901","doi":"https://doi.org/10.2196/101076","title":"Nonlinear Feature Modeling Using Kolmogorov-Arnold Networks in Electroencephalography for Depression Classification: Deep Learning Study","display_name":"Nonlinear Feature Modeling Using Kolmogorov-Arnold Networks in Electroencephalography for Depression Classification: Deep Learning Study","publication_year":2026,"publication_date":"2026-10-19","ids":{"openalex":"https://openalex.org/W7222923901","doi":"https://doi.org/10.2196/101076","pmid":"https://pubmed.ncbi.nlm.nih.gov/42896101","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13679874"},"language":"en","primary_location":{"id":"doi:10.2196/101076","doi":"https://doi.org/10.2196/101076","is_oa":true,"landing_page_url":"https://doi.org/10.2196/101076","pdf_url":null,"source":{"id":"https://openalex.org/S4387286577","display_name":"JMIR AI","issn_l":"2817-1705","issn":["2817-1705"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR AI","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.2196/101076","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159075426","display_name":"Bin Yang","orcid":"https://orcid.org/0009-0000-9202-3493","observed_orcids":["https://orcid.org/0009-0000-9202-3493"]},"institutions":[{"id":"https://openalex.org/I4210113703","display_name":"Henan University of Urban Construction","ror":"https://ror.org/01x1skr92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113703"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771"],"raw_orcid":"https://orcid.org/0009-0000-9202-3493","affiliations":[{"raw_affiliation_string":"Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771","institution_ids":["https://openalex.org/I4210113703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111335137","display_name":"YONGFENG FAN","orcid":"https://orcid.org/0009-0001-0046-0125","observed_orcids":["https://orcid.org/0009-0001-0046-0125"]},"institutions":[{"id":"https://openalex.org/I4210113703","display_name":"Henan University of Urban Construction","ror":"https://ror.org/01x1skr92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113703"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongfeng Fan","raw_affiliation_strings":["Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771"],"raw_orcid":"https://orcid.org/0009-0001-0046-0125","affiliations":[{"raw_affiliation_string":"Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771","institution_ids":["https://openalex.org/I4210113703"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159079057","display_name":"Xinhong Wei","orcid":"https://orcid.org/0009-0003-7180-0654","observed_orcids":["https://orcid.org/0009-0003-7180-0654"]},"institutions":[{"id":"https://openalex.org/I4210113703","display_name":"Henan University of Urban Construction","ror":"https://ror.org/01x1skr92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113703"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xinhong Wei","raw_affiliation_strings":["Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771"],"raw_orcid":"https://orcid.org/0009-0003-7180-0654","affiliations":[{"raw_affiliation_string":"Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771","institution_ids":["https://openalex.org/I4210113703"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5159066046","display_name":"Chunlei Shi","orcid":"https://orcid.org/0009-0003-5915-2914","observed_orcids":["https://orcid.org/0009-0003-5915-2914"]},"institutions":[{"id":"https://openalex.org/I4210113703","display_name":"Henan University of Urban Construction","ror":"https://ror.org/01x1skr92","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210113703"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunlei Shi","raw_affiliation_strings":["Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771"],"raw_orcid":"https://orcid.org/0009-0003-5915-2914","affiliations":[{"raw_affiliation_string":"Henan University of Urban Construction, No. 207, Longxiang Avenue, Xincheng District, Pingdingshan, Henan, 467000, China, 86 13837588771","institution_ids":["https://openalex.org/I4210113703"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5159079057"],"corresponding_institution_ids":["https://openalex.org/I4210113703"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76072863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"e101076","last_page":"e101076"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8838000297546387,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.8838000297546387,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.11089999973773956,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11071","display_name":"Treatment of Major Depression","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology (medical)"},"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/depression-classification","display_name":"depression classification","score":0.9350000023841858},{"id":"https://openalex.org/keywords/electroencephalography","display_name":"EEG","score":0.9190000295639038},{"id":"https://openalex.org/keywords/kolmogorov-arnold-networks","display_name":"Kolmogorov-Arnold networks","score":0.9959999918937683},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5170000195503235}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7599999904632568},{"id":"https://openalex.org/C522805319","wikidata":"https://www.wikidata.org/wiki/Q179965","display_name":"Electroencephalography","level":2,"score":0.7254999876022339},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6812999844551086},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6378999948501587},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6365000009536743},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5253000259399414},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4864000082015991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46050000190734863},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4472000002861023},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4471000134944916},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C2776867660","wikidata":"https://www.wikidata.org/wiki/Q1814941","display_name":"Depression (economics)","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.2196/101076","doi":"https://doi.org/10.2196/101076","is_oa":true,"landing_page_url":"https://doi.org/10.2196/101076","pdf_url":null,"source":{"id":"https://openalex.org/S4387286577","display_name":"JMIR AI","issn_l":"2817-1705","issn":["2817-1705"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR AI","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13679874","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13679874/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13679874/pdf/ai-v5-e101076.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR AI","raw_type":"Text"},{"id":"pmid:42896101","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42896101","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,"listed_in":[],"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":"JMIR AI","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:d817e962bf3d4fd78bea936e8bed1f8b","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/d817e962bf3d4fd78bea936e8bed1f8b","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"JMIR AI, Vol 5, Pp e101076-e101076 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.2196/101076","doi":"https://doi.org/10.2196/101076","is_oa":true,"landing_page_url":"https://doi.org/10.2196/101076","pdf_url":null,"source":{"id":"https://openalex.org/S4387286577","display_name":"JMIR AI","issn_l":"2817-1705","issn":["2817-1705"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","ki-jl-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"JMIR AI","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8048999905586243}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7222923901.pdf","grobid_xml":"https://content.openalex.org/works/W7222923901.grobid-xml"},"referenced_works_count":61,"referenced_works":["https://openalex.org/W4323922987","https://openalex.org/W4376128380","https://openalex.org/W4376631681","https://openalex.org/W4377823499","https://openalex.org/W4378473320","https://openalex.org/W4386968638","https://openalex.org/W4387405030","https://openalex.org/W4387972836","https://openalex.org/W4388089518","https://openalex.org/W4388304090","https://openalex.org/W4388606133","https://openalex.org/W4389117655","https://openalex.org/W4389611052","https://openalex.org/W4390708023","https://openalex.org/W4390955332","https://openalex.org/W4391173359","https://openalex.org/W4391304743","https://openalex.org/W4391804768","https://openalex.org/W4392172671","https://openalex.org/W4392356117","https://openalex.org/W4392878050","https://openalex.org/W4392917621","https://openalex.org/W4393621265","https://openalex.org/W4395096566","https://openalex.org/W4396601415","https://openalex.org/W4396821501","https://openalex.org/W4396941461","https://openalex.org/W4398249769","https://openalex.org/W4399258641","https://openalex.org/W4399591661","https://openalex.org/W4399855178","https://openalex.org/W4400019258","https://openalex.org/W4400439206","https://openalex.org/W4401111231","https://openalex.org/W4402366367","https://openalex.org/W4402661601","https://openalex.org/W4402901683","https://openalex.org/W4403089752","https://openalex.org/W4403600946","https://openalex.org/W4403826895","https://openalex.org/W4403867810","https://openalex.org/W4403947199","https://openalex.org/W4404113223","https://openalex.org/W4404417924","https://openalex.org/W4406172451","https://openalex.org/W4406260310","https://openalex.org/W4406662647","https://openalex.org/W4406761689","https://openalex.org/W4406941044","https://openalex.org/W4408564864","https://openalex.org/W4408648489","https://openalex.org/W4409761227","https://openalex.org/W4410084151","https://openalex.org/W4410342569","https://openalex.org/W4410887575","https://openalex.org/W4412081519","https://openalex.org/W4412353396","https://openalex.org/W4412582871","https://openalex.org/W4414651991","https://openalex.org/W6959705997","https://openalex.org/W7125974768"],"related_works":["https://openalex.org/W4416386388","https://openalex.org/W4415917779","https://openalex.org/W4403452237","https://openalex.org/W3165790662","https://openalex.org/W4285176209","https://openalex.org/W7213982471","https://openalex.org/W7168275268","https://openalex.org/W7128817080","https://openalex.org/W7219309217","https://openalex.org/W1879995557"],"abstract_inverted_index":{"Abstract":[0],"Background":[1],"Depression":[2],"is":[3],"a":[4,43,61,290],"common":[5],"mental":[6,11],"disorder":[7],"that":[8,202,252],"seriously":[9],"affects":[10],"health":[12],"and":[13,25,85,122,148,168,180,193,218,238,292,299,308],"quality":[14],"of":[15,57,77,90,112,262,277],"life.":[16],"Traditional":[17],"depression":[18,48,83,105,284,313],"diagnosis":[19,39],"mainly":[20],"relies":[21],"on":[22,95,164,245],"clinical":[23,309],"interviews":[24],"subjective":[26],"assessments,":[27],"which":[28],"may":[29],"lead":[30],"to":[31,73,86,107,151,282],"variability":[32],"in":[33,311],"diagnostic":[34],"outcomes.":[35],"Electroencephalogram":[36],"(EEG)-based":[37],"computer-aided":[38,312],"has":[40],"emerged":[41],"as":[42],"promising,":[44],"objective":[45],"approach":[46,294],"for":[47,64,81,131,295,304],"recognition.":[49,285,314],"However,":[50],"effectively":[51,256],"modeling":[52,93,154,280],"the":[53,75,88,165,169,203,224,230,246,258,275],"complex":[54,259],"nonlinear":[55,92,109,153,260,279],"characteristics":[56,261],"EEG":[58,97,132,208,263,296],"signals":[59,264],"remains":[60],"major":[62],"challenge":[63],"existing":[65],"deep":[66],"learning":[67,183,221],"methods.":[68],"Objective":[69],"This":[70,271],"study":[71,272],"aimed":[72],"investigate":[74],"applicability":[76,276],"Kolmogorov-Arnold":[78],"Networks":[79],"(KANs)":[80],"EEG-based":[82,104,283],"recognition":[84,106],"evaluate":[87],"effectiveness":[89],"KAN-based":[91,118,204,253,278],"frameworks":[94,205,281],"frequency-domain":[96],"features.":[98],"Methods":[99],"KAN":[100],"was":[101,172,187],"introduced":[102],"into":[103],"enhance":[108],"feature":[110],"representation":[111],"brain":[113],"electrical":[114],"signals.":[115],"Two":[116],"representative":[117],"convolutional":[119,176,214],"architectures,":[120],"ConvKAN":[121,126,227],"its":[123],"efficiency-optimized":[124],"variant":[125],"Efficient,":[127],"were":[128,162],"systematically":[129,273],"investigated":[130],"classification":[133,209],"tasks.":[134],"These":[135],"architectures":[136,254],"incorporated":[137],"KANLinear":[138],"layers":[139],"together":[140],"with":[141,174,212,234],"dynamic":[142],"grid":[143],"updating,":[144],"enhanced":[145],"weight":[146],"initialization,":[147],"regularization":[149],"strategies":[150],"improve":[152],"capability":[155],"while":[156,265],"maintaining":[157],"computational":[158,268],"efficiency.":[159,269],"Extensive":[160],"experiments":[161],"conducted":[163],"EDRA":[166,247],"dataset,":[167],"proposed":[170,287],"framework":[171,288],"compared":[173,211],"conventional":[175,213],"neural":[177,215],"network\u2013based":[178,216],"methods":[179,217],"traditional":[181,219],"machine":[182,220],"approaches.":[184,222],"Model":[185],"performance":[186,210],"evaluated":[188,225],"using":[189],"accuracy,":[190,235],"precision,":[191,236],"recall,":[192,237],"F":[194,239],"1":[195,240],"-score":[196,241],"metrics.":[197],"Results":[198],"Experimental":[199],"results":[200,250],"demonstrated":[201],"achieved":[206,229],"superior":[207],"Among":[223],"models,":[226],"Efficient":[228],"best":[231],"overall":[232],"performance,":[233],"all":[242],"exceeding":[243],"94%":[244],"dataset.":[248],"The":[249,286],"indicate":[251],"can":[255],"capture":[257],"preserving":[266],"high":[267],"Conclusions":[270],"demonstrates":[274],"provides":[289],"scalable":[291],"effective":[293],"signal":[297],"analysis":[298],"offers":[300],"valuable":[301],"technical":[302],"support":[303],"both":[305],"theoretical":[306],"research":[307],"applications":[310]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-20T00:00:00"}