dc.contributor.author | Kutlu, Yakup | |
dc.contributor.author | Yayık, Apdullah | |
dc.contributor.author | Yıldırım, Esen | |
dc.contributor.author | Yıldırım, Serdar | |
dc.date.accessioned | 12.07.201910:50:10 | |
dc.date.accessioned | 2019-07-12T22:05:51Z | |
dc.date.available | 12.07.201910:50:10 | |
dc.date.available | 2019-07-12T22:05:51Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Kutlu, Y., Yayık, A., Yildirim, E., Yildirim, S. (2019). LU triangularization extreme learning machine in EEG cognitive task classification. Neural Computing and Applications, 31 (4), pp. 1117-1126.
https://doi.org/10.1007/s00521-017-3142-1 | |
dc.identifier.issn | 0941-0643 | |
dc.identifier.issn | 1433-3058 | |
dc.identifier.uri | https://doi.org/10.1007/s00521-017-3142-1 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12508/562 | |
dc.description | WOS: 000466772500013 | en_US |
dc.description.abstract | Electroencephalography (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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.isversionof | 10.1007/s00521-017-3142-1 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Cognitive processes | en_US |
dc.subject | Lower-upper triangularization | en_US |
dc.subject | Extreme learning machine | en_US |
dc.subject | MoDP method | en_US |
dc.subject | Optimized nodes | en_US |
dc.subject.classification | Computer Science | |
dc.subject.classification | Artificial Intelligence | |
dc.subject.classification | Emotion Recognition | Electroencephalography | Brain Computer Interface | |
dc.subject.other | Brain | |
dc.subject.other | Cognitive systems | |
dc.subject.other | Electroencephalography | |
dc.subject.other | Electrophysiology | |
dc.subject.other | Knowledge acquisition | |
dc.subject.other | Singular value decomposition | |
dc.subject.other | Time domain analysis | |
dc.subject.other | Arithmetic tasks | |
dc.subject.other | Cognitive process | |
dc.subject.other | Difference values | |
dc.subject.other | Healthy subjects | |
dc.subject.other | Independent analysis | |
dc.subject.other | Learning systems | |
dc.title | LU triangularization extreme learning machine in EEG cognitive task classification | en_US |
dc.type | article | en_US |
dc.relation.journal | Neural Computing and Applications | en_US |
dc.contributor.department | Mühendislik ve Doğa Bilimleri Fakültesi -- Bilgisayar Mühendisliği Bölümü | en_US |
dc.identifier.volume | 31 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.startpage | 1117 | en_US |
dc.identifier.endpage | 1126 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.contributor.isteauthor | Kutlu, Yakup | |
dc.relation.index | Web of Science - Scopus | en_US |
dc.relation.index | Web of Science Core Collection - Science Citation Index Expanded | |