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Removing EEG Artifacts Using Spatially Constrained Independent Component Analysis and Daubechies Wavelet Based Denoising with Otsu’ Thresholding Technique

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Perception and Machine Intelligence (PerMIn 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7143))

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Abstract

ElectroEncephaloGram (EEG) records contains data regarding abnormalities or responses to some stimuli in the human brain. Such rhythms are examined by physicians for the purpose of detecting the neural disorders and cerebral pathologies. Because to the occurrences of artifacts, it is complicated to examine the EEG, for they introduce spikes which can be confused with neurological rhythms. Therefore, noise and undesirable signals must be removed from the EEG to guarantee a correct examination and diagnosis. This paper presents a novel technique for removing the artifacts from the ElectroEncephaloGram (EEG) signals. This paper uses Spatially-Constrained Independent Component Analysis (SCICA) to separate the exactly the artificate Independent Components (ICs) from the initial EEG signal. Then, Wavelet Denoising is applied to eliminate the brain activity from extracted artifacts, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. This paper uses Daubechies wavelet transform for wavelet denoising. Here, thresholding plays an important role in deciding the artifacts. Therefore, a better thresholding technique called Otsu’, thresholding is applied. Experimental result shows that the proposed technique results in better removal of artifacts.

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References

  1. Shao, S.-Y., Shen, K.-Q., Ong, C.J., Wilder-Smith, E., Li, X.-P.: Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach with Error Correction. IEEE Transactions on Biomedical Engineering 56, 336–344 (2009)

    Article  Google Scholar 

  2. Shao, S.-Y., Shen, K.-Q., Ong, C.-J., Li, X.-P., Wilder-Smith, E.P.V.: Automatic identification and removal of artifacts in EEG using a probabilistic multi-class SVM approach with error correction. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1134–1139 (2008)

    Google Scholar 

  3. Kavitha, P.T., Lau, C.T., Premkumar, A.B.: Modified ocular artifact removal technique from EEG by adaptive filtering. In: International Conference on Information, Communications & Signal Processing, pp. 1–5 (2007)

    Google Scholar 

  4. Kim, K.H., Yoon, H.W., Park, H.W.: Improved algorithm for ballistocardiac artifact removal from EEG simultaneously recorded with fMRI. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 936–939 (2004)

    Google Scholar 

  5. LeVan, P., Urrestarrazu, E., Gotman, J.: A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification. Clinical Neurophysiology 117(4), 912–927 (2006)

    Article  Google Scholar 

  6. Croft, R.J., Barry, R.J.: Removal of ocular artifact from the EEG: a review. Clinical Neurophysiology 30(1), 5–19 (2000)

    Article  Google Scholar 

  7. Joyce, C., Gorodnitsky, I., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Phychophysiology 41(2), 313–325 (2004)

    Article  Google Scholar 

  8. Krishnaveni, V., Jayaraman, S., Aravind, S., Hariharasudhan, V., Ramadoss, K.: Automatic identification and Removal of ocular artifacts from EEG using Wavelet transform. Measurement Science Review 6(4), 45–57 (2006)

    Google Scholar 

  9. Krishnaveni, V., Jayaraman, S., Malmurugan, N., Kandasamy, A., Ramadoss, D.: Non adaptive thresholding methods for correcting ocular artifacts in EEG. Academic Open Internet Journal 13 (2004)

    Google Scholar 

  10. Yuval-Greenberg, S., Tomer, O., Keren, A.S., Nelken, I., Deouell, L.Y.: Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades. Neuron 58(3), 429–441 (2008)

    Article  Google Scholar 

  11. Verobyov, S., Cichocki, A.: Blind noise reduction of multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biological Cybernetics 86, 293–303 (2002)

    Article  MATH  Google Scholar 

  12. Potter, M., Gadhok, N., Kinsner, W.: Separation performance of ICA on simulated EEG and ECG signals contaminated by noise. Canadian Journal of Electrical and Computer Engineering 27(3), 123–127 (2002)

    Google Scholar 

  13. Choi, S., Cichocki, A., Park, H., Lee, S.: Blind Source Separation and Independent Component Analysis: A Review. Neural Information Processing – Letters and Reviews 6(1) (January 2005)

    Google Scholar 

  14. Cichocki, A., Amari, S.-I.: Adaptative blind Signal and Image Processing Learning Algorithms and Applications. John Wiley & Sons, Ltd. (2002)

    Google Scholar 

  15. Sutherland, M.T., Tang, A.C.: Blind source separation can recover systematically distributed neuronal sources from “resting” EEG. In: Proceedings of the Second International Symposium on Communications, Control, and Signal Processing (ISCCSP 2006), Marrakech, Morocco, March 13-15 (2006)

    Google Scholar 

  16. Kierkels, J.J.M., Van Botel, G.J.M., Vogten, L.L.M.: A Model-Based Objective Evaluation of Eye Movement Correction in EEG Recordings. IEEE Transactions on Biomedical Engineering 53(2) (February 2006)

    Google Scholar 

  17. Akhtar, M.T., James, C.J.: Focal Artifact Removal from Ongoing EEG – A Hybrid Approach Based on Spatially-Constrained ICA and Wavelet De-noising. In: Annual International Conference of the IEEE EMBS Minneapolis, pp. 4027–4030 (2009)

    Google Scholar 

  18. Castellanos, N.P., Makarov, V.A.: Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis. J. Neuroscience Methods 158, 300–312 (2006)

    Article  Google Scholar 

  19. Wang, J.Z., Wiederhold, G., Firschein, O., Wei, S.X.: Content-based image indexing and searching using Daubechies’ wavelets. Int. J. Digit. Libr. 1, 311–328 (1997)

    Article  Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Geetha, G., Geethalakshmi, S.N. (2012). Removing EEG Artifacts Using Spatially Constrained Independent Component Analysis and Daubechies Wavelet Based Denoising with Otsu’ Thresholding Technique. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_43

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  • DOI: https://doi.org/10.1007/978-3-642-27387-2_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27386-5

  • Online ISBN: 978-3-642-27387-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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