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Data Science Modeling and Constraint-Based Data Selection for EEG Signals Denoising Using Wavelet Transforms

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Advances in Intelligent Systems Research and Innovation

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 379))

Abstract

This work presents basic information about Electroencephalogram (EEG) signals, their processing and application in practice. Modeling and constraint satisfaction cases have been considered aiming at diminishing the manual labor during the wavelet signal filtering and fitting to medical applications. The EEG signals are easily affected by various noise sources. The noise can be electrode noise or can be generated from the body itself. The noises in the EEG signals are called artifacts and these artifacts are needed to be removed from the original EEG signals for the proper analysis of the signals. This work presents denoising algorithm based on the combination of wavelet transform (WT), threshold processing and inverse wavelet transform. The proposed algorithm is tested using real EEG signals. To improve its efficiency, different modeling and data preprocessing methods have been applied. In case when there is a need of constraint shift/modification/elimination, new types of constraints are considered and applied.

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Acknowledgements

This work is supported by the Bulgarian National Science Fund, Project title “Synthesis of a dynamic model for assessing the psychological and physical impacts of excessive use of smart technologies”, KP-06-N 32/4/07.12.2019 and by Project No. AP09259370 “Development of a technological platform for virtual learning based on artificial intelligence approaches” due to grant funding from the Ministry of Education and Science of the Republic of Kazakhstan.

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Authors and Affiliations

  1. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Magdalena Garvanova, Ivan Garvanov & Vladimir Jotsov

  2. Al-Farabi Kazakh National University, Almati, Kazakhstan

    Ivan Garvanov & Vladimir Jotsov

Authors
  1. Magdalena Garvanova
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  2. Ivan Garvanov
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  3. Vladimir Jotsov
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Correspondence to Vladimir Jotsov .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev

  2. University of Library Studies and Information Technologies, Sofia, Bulgaria

    Vladimir Jotsov

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Garvanova, M., Garvanov, I., Jotsov, V. (2022). Data Science Modeling and Constraint-Based Data Selection for EEG Signals Denoising Using Wavelet Transforms. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Advances in Intelligent Systems Research and Innovation. Studies in Systems, Decision and Control, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-78124-8_11

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