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Comparative Analysis of ICA, PCA-Based EASI and Wavelet-Based Unsupervised Denoising for EEG Signals

  • Ankita Bhatnagar
  • Krushna Gupta
  • Utkarsh Pandharkar
  • Ramchandra Manthalkar
  • Narendra Jadhav
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)

Abstract

Electroencephalography (EEG) can be used to study various brain activities related to human responses and disorders. EEG signal is prone to noises which are caused due to eye movements, power-line interference, muscle movements, etc. Therefore, to obtain refined EEG signals for further processing, it should be denoised. There are several methods by which EEG signals can be denoised, among which we have used Independent Component Analysis (ICA), Principal Component Analysis (PCA)-based Equivariant Adaptive Separation by Independence (EASI), and Wavelet-based unsupervised denoising methods. The performance of these methods is compared using Signal-to-Noise Ratio (SNR) and Percentage Root-mean-square Difference (PRD).

Keywords

EEG Denoising ICA PCA-based EASI Wavelet 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Ankita Bhatnagar
    • 1
  • Krushna Gupta
    • 1
  • Utkarsh Pandharkar
    • 1
  • Ramchandra Manthalkar
    • 1
  • Narendra Jadhav
    • 1
  1. 1.Shri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia

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