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LU triangularization extreme learning machine in EEG cognitive task classification

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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.

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Acknowledgements

The authors are very grateful to Mr. Server Göksel Eraldemir for his efforts to construct the EEG-MaTeP database.

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Authors and Affiliations

  1. Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey

    Yakup Kutlu

  2. Alparslan Defence Sciences, National Defense University, Ankara, Turkey

    Apdullah Yayık

  3. Department of Electrical and Electronics Engineering, Adana Science and Technology University, Adana, Turkey

    Esen Yildirim

  4. Department of Computer Engineering, Adana Science and Technology University, Adana, Turkey

    Serdar Yildirim

Authors
  1. Yakup Kutlu
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  2. Apdullah Yayık
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  3. Esen Yildirim
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  4. Serdar Yildirim
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Correspondence to Yakup Kutlu.

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Kutlu, Y., Yayık, A., Yildirim, E. et al. LU triangularization extreme learning machine in EEG cognitive task classification. Neural Comput & Applic 31, 1117–1126 (2019). https://doi.org/10.1007/s00521-017-3142-1

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  • DOI: https://doi.org/10.1007/s00521-017-3142-1

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