LU triangularization extreme learning machine in EEG cognitive task classification

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dc.authoridYILDIRIM, Serdar/0000-0003-3151-9916
dc.contributor.authorKutlu, Yakup
dc.contributor.authorYayik, Apdullah
dc.contributor.authorYildirim, Esen
dc.contributor.authorYildirim, Serdar
dc.date.accessioned2025-01-06T17:37:01Z
dc.date.available2025-01-06T17:37:01Z
dc.date.issued2019
dc.description.abstractElectroencephalography (EEG) has been used as a promising tool for investigation of brain activity during cognitive processes. The aim of this study is to reveal whether EEG signals can be used for classifying cognitive processes: arithmetic tasks and text reading. A recently introduced EEG database, which is constructed from 18 healthy subjects during a slide show including 60 slides of simple arithmetic tasks and easily readable texts, is used for this purpose. Multi-order difference plot-based time-domain attributes, number of values in specified regions after scattering the sequential difference values with several degrees, are extracted. For classification, improved extreme learning machine (ELM) scheme, namely luELM, by the use of lower-upper triangularization method instead of singular value decomposition which has disadvantages when used with huge data is proposed. As a result, higher accuracy results are achieved with reduced training time for proposed luELM classifier than traditional ELM classifier for both subject-dependent and subject-independent analysis.
dc.identifier.doi10.1007/s00521-017-3142-1
dc.identifier.endpage1126
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85021882746
dc.identifier.scopusqualityQ1
dc.identifier.startpage1117
dc.identifier.urihttps://doi.org/10.1007/s00521-017-3142-1
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2083
dc.identifier.volume31
dc.identifier.wosWOS:000466772500013
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectCognitive processes
dc.subjectLower-upper triangularization
dc.subjectExtreme learning machine
dc.subjectMoDP method
dc.subjectOptimized nodes
dc.titleLU triangularization extreme learning machine in EEG cognitive task classification
dc.typeArticle

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