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

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


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).


EEG Denoising ICA PCA-based EASI Wavelet 


  1. 1.
    Cardoso, J.-F., Laheld, B.H.: Equivariant adaptive source separation. IEEE Trans. Sig. Process. 44(12) (1996)CrossRefGoogle Scholar
  2. 2.
    Mahajan, R., Morshed, B.I.: Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and wavelet-ICA. IEEE J. Biomed. Health Inform. 19(1) (2015)CrossRefGoogle Scholar
  3. 3.
    Hazra, T.K., Guhathakurta, R.: Comparing wavelet and wavelet packet image denoising using thresholding techniques. Int. J. Sci. Res. (IJSR) 5(6) (2016)Google Scholar
  4. 4.
    Senthil Kumar, P., Arumuganathan, R., Sivakumar, K., Vimal, C.: Removal of ocular artifacts in the EEG through wavelet transform without using an EOG reference channel. Int. J. Open Prob. Comput. Math. 1(3) (2008)Google Scholar
  5. 5.
    Jadhav, N., Manthalkar, R., Joshi, Y.: Effect of meditation on emotional response: an EEG-based study. Biomed. Sig. Process. Control 34, 101–113 (2017)CrossRefGoogle Scholar
  6. 6.
    Makeig, S., Bell, A.J., Jung, T.-P., Sejnowski, T.J.: Independent component analysis of electroencephalographic data. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 1995), vol. 8 (1995)Google Scholar
  7. 7.
    Hyv¨arinen, A., Karhunen, J., Oja, E.: Independent component analysis. Wiley (2001)Google Scholar
  8. 8.
    Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Netw. 13(4–5) (2000)CrossRefGoogle Scholar
  9. 9.
    Dong Kang, F., Luo Zhizeng, S.: A method of denoising multi-channel EEG signals fast based on PCA and DEBSS Algorithm. 2012 International Conference on Computer Science and Electronics Engineering, (2012)Google Scholar
  10. 10.
    Simranpreet Kaur, F., Sheenam Malhotra, S.: Various Techniques for Denoising EEG signal: A Review. International Journal Of Engineering and Computer Science ISSN:2319-7242 Volume 3 Issue Page No. 7965-7973, (2014)Google Scholar
  11. 11.
    Tibshirani, R.: Stein’s unbiased risk estimate. Statistical Machine Learning. Springer (2015)Google Scholar
  12. 12.
    Khatwani, P., Tiwari, A.: Removal of noise from EEG signals using cascaded filter—wavelet transforms method. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 3(12) (2014)CrossRefGoogle Scholar
  13. 13.
    Estrada, E., Nazeran, H., Sierra, G., Ebrahimi, F., Mikaeili, M.: Wavelet EEG denoising for automatic sleep stage classification (2011).
  14. 14.
    Walters-Williams, J., Li, Y.: Using invariant translation to denoise electroencephalogram signals. Am. J. Appl. Sci. 8(11), 1122–1130 (2011)CrossRefGoogle Scholar
  15. 15.
    Princy, R., Thamarai, P., Karthik, B.: Denoising EEG signal using wavelet transform. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 4(3) (2015)Google Scholar
  16. 16.
    Al-Qazzaz, N.K., Hamid Bin Mohd Ali, S., Ahmad, S.A., Islam, M.S., Escudero J.: Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task. Sensors 15, 29015–29035 (2015). Scholar
  17. 17.
    Garg, S., Narvey, R.: Denoising & feature extraction of EEG signal using wavelet transform. Int. J. Eng. Sci. Technol. 5(6) (2013)Google Scholar
  18. 18.
    Zheng-you, H.E., Xiaoqing, C., Guoming, L.: Wavelet entropy measure definition and its application for transmission line fault detection and identification. In: International Conference on Power System Technology (2006)Google Scholar
  19. 19.
    Delorme, A., Sejnowski, T., Makeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 34(4) (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

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

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