Efficient Denoising Schemes of EEG Signals

Elsherbieny, Zeinab; Messiha, Nagy; El-Fisawy, Adel S.; Rihan, Mohamed; Abd El-Samie, Fathi E.

doi:10.21608/mjeer.2019.77020

	Efficient Denoising Schemes of EEG Signals
Menoufia Journal of Electronic Engineering Research
Article 59, Volume 28, ICEEM2019-Special Issue, 2019, Page 209-213
Document Type: Original Article
DOI: 10.21608/mjeer.2019.77020
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Authors
Zeinab Elsherbieny; Nagy Messiha; Adel S. El-Fisawy; Mohamed Rihan; Fathi E. Abd El-Samie
Department Of Electronics And Communications Menoufia University Faculty Of Electronic Engneering,Menouf
Abstract
This paper presents a class of noise reduction techniques for EEG signals. The noise reduction is very important for subsequent EEG processing tasks. The suggested techniques are extended from application in speech processing to application in EEG signals due to the common nature of low frequency of both types of signals. These techniques are spectral subtraction, Wiener filtering, adaptive Wiener filtering, and Discrete Wavelet Transform (DWT). A comparison between different techniques is presented. Simulation results are used to compare between the different denoising techniques. Four metrics are used to evaluate the different denoising techniques: signal -to-noise ratio (SNR), segmental signal-to-noise ratio (SNRseg), spectral distortion (SD), and log likelihood ratio (LLR).
Keywords
EEG; denoising; Spectral Subtraction; Wiener filter; Adaptive Wiener filter; Discrete Wavelet Transform


References
[1] Mohammad Ziaullah, Zuber Ahmed Punekar, Ujwala and M. Megha4 Bhagyashri,” Denoising of EEG Signals For Analysis of Brain Disorders: A Review,” International Research Journal of Engineering and Technology (IRJET), vol. 4, Issue: 07 , July -2017. [2] Simranpreet Kaur1, Sheenam Malhotra et al., “Various Techniques for Denoising EEG signal: A Review,” International Journal Of Engineering And Computer Science, vol. 3, ISSN 8, pp.2319-7242, August 2014. [3] J Priyanka Khatwani and Archana Tiwari, “A Survey on Different Noise Removal Techniques Of EEG Signals,” International Journal of Advanced Research in Computer and Communication Engineering, vol.2, issue 2, pp. 1091-95, February 2013. [4] N.V Kalpakam and Venkataramanan, “Haar wavelet decomposition of EEG signal for ocular artifact de-noising a mathematical analysis, Circuits and Systems” NEWCAS 2004. The 2nd Annual IEEE Northeast Workshop , vol. no., pp. 141- 144, Issue: 07 , July -2017. [5] Eleni Kroupi, Ashkan Yazdani et al., “Ocular Artifact Removal From EEG : A Comparison Of Subspace Projection And Adaptive Filtering Methods,” 19th European Signal Processing Conference, Barcelona, Spain, 2011. [6] M. BALAMAREESWARAN and D. EBENEZER et al., “Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization,” BALAMAREESWARAN & EBENEZER, Biomed. & Pharmacol. J., vol. 8, pp. 399-406, 2015. [7] Priyanka Khatwani, Archana Tiwari, “ASurvey on Different Noise Removal Techniques Of EEG Signals,”International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, issue2, pp. 1091-95, February 2013. [8] Jeena Joy, Salice Peter et al., “Denoising Using Soft Thresholding,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol .2, issue 3, pp. 1027-1031, March 2013. [9] Patil Suhas S.et al., “Wavelet Transform to Advance the Quality of EEG Signals in Biomedical Analysis,” 3rd International Conference On Computing Communication Systems and Networking Technologies, Coimbatore, pp.26- 28, July 2012. [10] Eleni Kroupi, Ashkan Yazdani et al., “Ocular Artifact Removal From EEG : A Comparison Of Subspace Projection And Adaptive Filtering Methods,” 19thEuropean Signal Processing Conference, Barcelona, Spain, 2011. [11] Janett Walters-Williams & Yan Li, A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA, International Journal of Biometrics and Bioinformatics , Vol.5, Issue (2) , 2011. [12] LanlanYu; EEG De-Noising Based on Wavelet Transformation, Bioinformatics and Biomedical Engineering,. ICBBE. 3rd International Conference, pp.1-4, 11-13, June 2009. [13] M. Z. Parvez, and M. Paul," EEG Signal Classification Using Frequency Band Analysis Towards Epileptic Seizure Prediction," International Conference Computer and Information Technology, pp. 126-130, 2014. [14]Ali yener Multilu," EEG Signal Classification Using Frequency Band Analysis Towards Epileptic Seizure Prediction," Biomedical Signal Processing and Control, pp. 33-40, 2018 .
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Efficient Denoising Schemes of EEG Signals

[1] Mohammad Ziaullah, Zuber Ahmed Punekar, Ujwala and M. Megha4 Bhagyashri,” Denoising of EEG Signals For Analysis of Brain Disorders: A Review,” International Research Journal of Engineering and Technology (IRJET), vol. 4, Issue: 07 , July -2017.

[2] Simranpreet Kaur1, Sheenam Malhotra et al., “Various Techniques for Denoising EEG signal: A Review,” International Journal Of Engineering And Computer Science, vol. 3, ISSN 8, pp.2319-7242, August 2014.

[3] J Priyanka Khatwani and Archana Tiwari, “A Survey on Different Noise Removal Techniques Of EEG Signals,” International Journal of Advanced Research in Computer and Communication Engineering, vol.2, issue 2, pp. 1091-95, February 2013.

[4] N.V Kalpakam and Venkataramanan, “Haar wavelet decomposition of EEG signal for ocular artifact de-noising a mathematical analysis, Circuits and Systems” NEWCAS 2004. The 2nd Annual IEEE Northeast Workshop , vol. no., pp. 141- 144, Issue: 07 , July -2017.

[5] Eleni Kroupi, Ashkan Yazdani et al., “Ocular Artifact Removal From EEG : A Comparison Of Subspace Projection And Adaptive Filtering Methods,” 19th European Signal Processing Conference, Barcelona, Spain, 2011.

[6] M. BALAMAREESWARAN and D. EBENEZER et al., “Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization,” BALAMAREESWARAN & EBENEZER, Biomed. & Pharmacol. J., vol. 8, pp. 399-406, 2015.

[7] Priyanka Khatwani, Archana Tiwari, “ASurvey on Different Noise Removal Techniques Of EEG Signals,”International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, issue2, pp. 1091-95, February 2013.

[8] Jeena Joy, Salice Peter et al., “Denoising Using Soft Thresholding,” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol .2, issue 3, pp. 1027-1031, March 2013.

[9] Patil Suhas S.et al., “Wavelet Transform to Advance the Quality of EEG Signals in Biomedical Analysis,” 3rd International Conference On Computing Communication Systems and Networking Technologies, Coimbatore, pp.26- 28, July 2012.

[10] Eleni Kroupi, Ashkan Yazdani et al., “Ocular Artifact Removal From EEG : A Comparison Of Subspace Projection And Adaptive Filtering Methods,” 19thEuropean Signal Processing Conference, Barcelona, Spain, 2011.

[11] Janett Walters-Williams & Yan Li, A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wavelet and ICA, International Journal of Biometrics and Bioinformatics , Vol.5, Issue (2) , 2011.

[12] LanlanYu; EEG De-Noising Based on Wavelet Transformation, Bioinformatics and Biomedical Engineering,. ICBBE. 3rd International Conference, pp.1-4, 11-13, June 2009.