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A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Author

Listed:
  • Shalini Stalin
  • Vandana Roy
  • Prashant Kumar Shukla
  • Atef Zaguia
  • Mohammad Monirujjaman Khan
  • Piyush Kumar Shukla
  • Anurag Jain
  • A. M. Bastos Pereira

Abstract

The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

Suggested Citation

  • Shalini Stalin & Vandana Roy & Prashant Kumar Shukla & Atef Zaguia & Mohammad Monirujjaman Khan & Piyush Kumar Shukla & Anurag Jain & A. M. Bastos Pereira, 2021. "A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, October.
    • Handle: RePEc:hin:jnlmpe:2942808
    DOI: 10.1155/2021/2942808
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