LU triangularization extreme learning machine in EEG cognitive task classification

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...

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Bibliographic Details
Published inNeural computing & applications Vol. 31; no. 4; pp. 1117 - 1126
Main Authors Kutlu, Yakup, Yayık, Apdullah, Yildirim, Esen, Yildirim, Serdar
Format Journal Article
LanguageEnglish
Published London Springer London 01.04.2019
Springer Nature B.V
Subjects
Arithmetic
Artificial Intelligence
Brain
Classification
Classifiers
Cognition & reasoning
Cognitive tasks
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Electroencephalography
Image Processing and Computer Vision
Neural networks
Original Article
Probability and Statistics in Computer Science
Signal processing
Singular value decomposition
Optimized nodes
MoDP method
Cognitive processes
Extreme learning machine
Lower–upper triangularization
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Summary: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-017-3142-1