Performance analysis of IIR filter in removing PLI from EEG signal

  • Authors

    • Rayhan Habib Jibon Electronics and Communication Engineering, Khulna University
    • Etu Podder Electronics and Communication Engineering, Khulna University
    • Abdullah Al-Mamun Bulbul Electronics and Telecommunication Engineering, Bangabandhu Sheikh Mujibur Rahman Science & Technology University
    • Ramendra Nath Bairagi Electronics and Communication Engineering, Khulna University
    • Md. Salim Ahmed Electronics and Communication Engineering, Khulna University
    • Imtiaj Ahmmed Shohagh Electronics and Communication Engineering, Khulna University
    2019-03-28
    https://doi.org/10.14419/ijet.v7i4.26715
  • Chebyshev type II filter, EEG, Notch filter, PLI, SNR.

  • Electroencephalogram (EEG) is a non-incursive test and the electrical signals of the brain from the scalp is recorded by this test. Several diagnosis conditions (for example dizziness, epilepsy, head injuries, etc.) are checked by this test. Moreover, the information about the brain death is also be acquainted by the EEG test. EEG signals inherit the bandwidth of 1 to 50 Hz. So, these can be easily contaminated by different artifacts (such as power line interference (PLI), eye blink artifact, and electromyogram artifact). Out of these artifacts, 50 Hz PLI is the most salient. In this paper, IIR filters (Notch filter and Chebyshev type II filter) are configured to remove the PLI. Through the subsequent utilization of these filters, the artifact can be removed from the EEG signals in a notable amount. Thereby this approach will ensure the true information about detecting brain diseases and possibilities to know how many portions of the main signal is released from the artifact. Investigating the simulation results that includes the output waveforms and SNR values, it can be concluded that the Notch filter performs better than Chebyshev type II filter. This paper presents a comparison between two digital (Notch, Chebyshev type II) filters for removing PLI from EEG signal and helps to choose the best one from these two filters.

     

     

  • References

    1. [1] Otto C, Milenkovic A, Sanders C & Jovanov E,â€System architecture of a wireless body area sensor network for ubiquitous health monitoringâ€, System architecture of a wireless body area sensor network for ubiquitous health monitoring, Vol.1, No.4, (2006), pp.307-26.

      [2] Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ & Vaughan TM,†Brain-computer interface technology: a review of the first international meetingâ€, IEEE transactions on rehabilitation engineering, Vol.8, No.2,.(2000), pp.164-73. https://doi.org/10.1109/TRE.2000.847807

      [3] Michel CM, Lehmann D, Henggeler B & Brandeis D,†Localization of the sources of EEG delta, theta, alpha and beta frequency bands using the FFT dipole approximationâ€, Electroencephalography and clinical neurophysiology, Vol.82, No.1, (1992), pp.38-44. https://doi.org/10.1016/0013-4694(92)90180-P.

      [4] Nacy SM, Kbah SN, Jafer HA & Al-Shaalan I,†Controlling a Servo Motor Using EEG Signals from the Primary Motor Cortexâ€, American Journal of Biomedical Engineering, Vol.6, No.5, (2016), pp.139-46.

      [5] Banu U, Patil DG & Fatima DR,†A Survey on Sources of Noise and Advanced Noise Removal Techniques of Biosignalsâ€, International Journal on Emerging Technologies, Vol.7, No.2, (2016), pp.8-13.

      [6] Ifeachor EC & Jervis BW (2001), Digital Signal Processing: A Practical Approach, 2nd edn. Prentice Hall, Upper Saddle River, New Jersey, USA, pp.38-41.

      [7] Dragosevic MV & Stankovic SS,†An Adaptive Notch Filter with Improved Tracking Propertiesâ€, IEEE Trans. Signal Processing, Vol.43, No.9, (1995), pp.2068–2078. https://doi.org/10.1109/78.414768.

      [8] He P, Wilson G & Russell C,†Removal of artifacts from electro-encephalogram by adaptive filteringâ€, Medical & biological engineering & computing, Vol.43, No.3, (2004), pp.407-12. https://doi.org/10.1007/BF02344717.

      [9] Kwong RH & Johnston EW,†A variable step size LMS algorithmâ€, IEEE Transactions on signal processing, Vol.40, No.7, (1992), pp.1633-42. https://doi.org/10.1109/78.143435.

      [10] Scientific Differences Website, available online: https://www.differencebetween.net/science/difference-between-iir-and-fir-filters, last visit:30.07.2018

      [11] Data Acquisition, Loggers, Amplifiers, Transducers, Electrodes Website, available online: https://www.biopac.com/knowledge-base/iir-vs-fir-filters, last visit:30.07.2018

      [12] Wang CM & Xiao W,†Second-order IIR Notch Filter Design and implementation of digital signal processing systemâ€, In Applied Mechanics and Materials, Vol.347, (2013), pp.729-32.

      [13] Bjaerum S, Torp H & Kristoffersen K,†Clutter filter design for ultrasound color flow imagingâ€, IEEE transactions on ultrasonics, ferroelectrics, and frequency control, Vol.49, No.2, (2002), pp.204-16. https://doi.org/10.1109/58.985705.

      [14] Vesanto J, Himberg J, Alhoniemi E & Parhankangas J,†Self-organizing map in Matlab: the SOM Toolboxâ€, Proceedings of the Matlab DSP conference, Vol.99, (1999), pp.16-17.

      [15] Ferdjallah M & Barr RE,†Adaptive digital notch filter design on the unit circle for the removal of power line noise from biomedical signalsâ€, IEEE Transactions on Biomedical Engineering, Vol.41, No.6, (1994), pp.529-36. https://doi.org/10.1109/10.293240.

      [16] Zhou W, Zhou J, Zhao H & Ju L, removing eye movement and power line artifacts from the EEG based on ICA. In Engineering in Medicine and Biology Society. IEEE-EMBS 27th Annual International Conference (2005), pp.6017-6020.

      [17] Kang JS, Kavuri S & Lee M,†Adaptive EEG noise filtering for coherence analysis: In Brain-Computer Interface (BCI)â€, IEEE International Winter Workshop, Vol.2, No.2, (2014), pp.1-4.

      [18] Bindal R, Kumar S & Kumar A (2014), Deknoising of Electroencephalogram Signals Using Digital Signal Processor, 1st edn. Krishi Sanskriti Publications, New Delhi, India, pp.38-41.

      [19] Sekyere SA (2015), Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis, 3rd edn. PeerJ, pp.1086.

      [20] Islam SMM & Farid MSU,†Denoising EEG Signal using Different Adaptive Filter Algorithmsâ€, International Journal of Enhanced Research in Science, Technology & Engineering, Vol.4, No.11, (2015), pp.49-55.

      [21] Kaushal G, Jain VK, & Singh A,†Removal of Power Line Interference from EEG using Wavelet-Icaâ€, Proceedings of International Conference on Advancements in Engineering and Technology ICAET, Vol.6, (2015), pp:29-31.

      [22] Khatter A, Bansal D & Mahajan R,†Study of Various Automatic EEG Artifact Removalâ€, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Vol.5, No.X, (2017), pp.1027-1037. https://doi.org/10.22214/ijraset.2017.10149.

      [23] Wang CM & Xiao WC,†Second-order IIR Notch Filter Design and implementation of digital signal processing systemâ€, In Applied Mechanics and Materials, Trans Tech Publications, Vol.347 (2013), pp.729-732. https://doi.org/10.2991/isccca.2013.144.

      [24] Proakis JG & Manolakis DG (1998), Digital Signal Processing, 4th edn. PHI publication, New Delhi, India, pp.701-707.

      [25] Tandra R & Sahai A,†SNR walls for signal detectionâ€, IEEE Journal of selected topics in Signal Processing, Vol.2 No.1, (2008), pp.4-17. https://doi.org/10.1109/JSTSP.2007.914879.

      [26] MIT-BIH Arrhythmia Database Website, available online: http://www.physionet.org/physiobank/database/mitdb, last visit:10.08.2018.

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  • How to Cite

    Habib Jibon, R., Podder, E., Al-Mamun Bulbul, A., Nath Bairagi, R., Salim Ahmed, M., & Ahmmed Shohagh, I. (2019). Performance analysis of IIR filter in removing PLI from EEG signal. International Journal of Engineering & Technology, 7(4), 5363-5367. https://doi.org/10.14419/ijet.v7i4.26715