Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet

Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet

Автор: Vandana Roy, Shailja Shukla

Журнал: International Journal of Modern Education and Computer Science (IJMECS) @ijmecs

Статья в выпуске: 7 vol.6, 2014 года.

Бесплатный доступ

This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG) signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the de?ation approach Spatially-Constrained Independent Component Analysis (SCICA) to separate the Independent Components (ICs) from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.

Еще

Artifacts removal, Biomedical Signal Filtering, Electroencephalogram (EEG), source separation, Spatially-Constrained Independent Component Analysis (SCICA), thresholding, daubechies wavelet

Короткий адрес: https://sciup.org/15014670

IDR: 15014670

Список литературы Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet

  • J.R. Wolpaw, N. Birbaumer, W.J. Heetderks, D.J. McFarland, P.H. Peckham, G. Schalk, E. Donchin, L.A. Quatrano, C.J. Robinson, and T.M. Vaughan. Brain-computer interface technology: A review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 8(2):164-173, June 2000.
  • T-P. Jung, C. Humphries, T.W. Lee, M.J. McKeown, V. Iragui, S. Makeig, and T.J. Sejnowski. Removing electroencephalographic artifacts by blind source separation. Psychophysiology, 37:163-178, 2000.
  • D.A. Overton and C. Shagass. Distribution of eye movement and eye blink potentials over the scalp. Electroencephalography and Clinical Neurophysiology, 27:546, 1969
  • J.F. Cardoso. High-order contrasts for independent component anslysis. Neural Computation, 11(1):157192, 1999.
  • P. Berg and M. Scherg. Dipole models of eye activity and its application to the removal of eye artifacts from the EEG and MEG. Clinical Physics and Physiological Measurements, 12(Supplement A):49-54, 1991
  • J.-F. Cardoso. High-order contrasts for independent component anslysis. Neural Computation, 11(1):157-192, 1999 .
  • A. Cichocki and S. Vorobyov. Application of ICA for automatic noise and interference cancellation. In Proceedings of the Second International Workshop on ICA and BSS, June 2000.
  • R. J. Croft and R. J. Barry. EOG correction: Which regression should we use? Psychophysiology, 37:123-125, 2000.
  • R. J. Croft and R. J. Barry. Removal of ocular artifact from the EEG: a review. Clinical Neurophysiology, 30(1):5-19, 2000.
  • Dr. Patti Davies. Personal communication. Occupational Therapy Department, Colorado State University.
  • G. Gratton, M.G. Coles, and E. Donchin. A new method for offline removal of ocular artifact. Electroencephalography and Clinical Neurophysiology, 55:468-484, 1983.
  • S.A. Hillyard and R. Galambos. Eye-movement artifact in the CNV. Electroencephalography and Clinical Neurophysiology, 28:173-182, 1970.
  • T.P. Jung, S. Makeig, M. Westerfield, J. Townsend, E. Courchesne, and T.J. Sejnowski. Removal of eye activity artifacts from visual eventrelated potentials in normal and clinical subjects. Clinical Neurophysiology, 111(10):1745-58, 2000.
  • J.L. Kenemans, P. Molenaar, M.N. Verbaten, and J.L. Slangen. Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data.Psychophysiology, 28:114-121, 1991.
  • Z.J. Koles. The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalography and Clinical Neurophysiology, 79:440-447, 1991.
  • S. Makeig, A.J. Bell, T-P. Jung, and T.J. Sejnowski. Independent component analysis of electroencephalographic data. In editors, Advances in neural information processing systems, volume 8, pages 145-151, Cambridge, MA, 1996. The MIT Press.
  • M. Potter, N. Gadhok, and W. Kinsner. Separation performance of ICA on simulated EEG and ECG signals contaminated by noise. Canadian Journal of Electrical and Computer Engineering, 27(3):123-127, July 2002.
  • M. Rahalova, P. Sykacek, M. Koska, and G. Dor?ner. Detection of the EEG artifacts by the means of the (extended) Kalman filter. Measurement Science Review, 1(1):59-62, 2001.
  • A. Schl?ogl, P. Anderer, S.J. Roberts, M. Pregenzer, and G. Pfurtscheller. Artefact detection in sleep EEG by the use of Kalman filtering. In Proceedings EMBEC'99, Part II, pages 1648-1649, Vienna, Austria, November 1999.
  • R. Verleger, T. Gasser, and J. Mocks. Correction of EOG artifacs in event-related potentials of EEG: Aspects of reliability and validity. Psychophysiology, 19:472-480, 1982.
  • S. Verobyov and A. Cichocki. Blind noise reduction of multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biological Cybernetics, 86:293-303, 2002.
  • L. Vigon, M.R. Saatchi, J.E.W. Mayhew, and R. Fernandes. Quantitative evaluation of techniques for ocular artefact removal. IEE Proc.-Sci. Meas. Technol., 147(5), September 2000.
  • P.D. Welch. The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodgrams. IEEE Transactions on Audio Electroacoustics, 15(2):70-73, June 1967.
  • J.L. Whitton, F. Lue, and H. Moldofsky. A spectral method for removing eye-movement artifacts from the EEG. Electroencephalography and Clinical Neurophysiology, 44:735-741, 1978.
  • J.C. Woestenburg, M.N. Verbaten, and J.L. Slangen. The removal of the eye-movement artifact from the EEG by regression analysis in the frequency domain. Biological Physiology, 16:127-147, 1983.
  • Kyung Hwan Kim, HyoWoon Yoon and Hyun Wook Park, "Improved algorithm for ballistocardiac artifact removal from EEG simultaneously recorded with fMRI", 26th Annual International Conference of the IEEE Engineering in Medicine andBiology Society, Vol.1, 2004, Pp. 936-939.
  • P. LeVan, E. Urrestarrazu, and J. Gotman, “A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification“, Clinical Neurophysiology, Vol. 117, No.4, 2006, pp. 912- 927.
  • R.J. Croft and R.J. Barry, “Removal of ocular artifact from the EEG: a review”, Clinical Neurophysiology, Vol. 30, No.1, 2000, pp. 5 – 19.
  • CA.Joyce, IF.Gorodnitsky, M.Kutas, “Automatic removal of eye movement and blink artifacts from EEG data using blindcomponent separation”,Psychophysiology. Vol. 41, No.2, 2004, pp.313- 325.
  • V. Krishnaveni, S. Jayaraman, S. Aravind, V. Hariharasudhan, K. Ramadoss, “Automatic identification and Removal of ocular artifacts from EEG using Wavelet transform “, Measurement Science Review, Vol. 6, No.4, 2006 pp.45-57.
  • V. Krishnaveni, S. Jayaraman, N. Malmurugan, A. Kandasamy, D. Ramadoss, “Non adaptive thresholding methods for correcting ocular artifacts in EEG” , Academic Open Internet Journal, Vol.13, 2004.
  • Shlomit Yuval-Greenberg, Orr Tomer, Alon S. Keren, Israel Nelken and Leon Y. Deouell, “Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades”, Neuron, Vol.58, No.3, 2008, pp.429- 441.
  • S. Verobyov and A. Cichocki. “Blind noise reduction of multisensory signals using ICA and subspace filtering, with application to EEG analysis”. Biological Cybernetics, 86:293-303, 2002.
  • S. Choi, A. Cichocki, H. Park, S. Lee, blind Source “Separation and Independent Component Analysis: A Review”, Neural Information Processing – Letters and Reviews, Vol. 6, no. 1, January 2005.
  • A. Cichocki, Shun-ichiAmari, “Adaptative blind Signal and Image Processing Learning Algorithms and Applications”, John Wiley & Sons, ltd, 2002.
  • Sutherland, M.T., and Tang A.C. “ Blind source separation can recover systematically distributed neuronal sources from “resting” EEG”, Proceedings of the Second International Symposium on Communications, Control, and Signal Processing(ISCCSP 2006), Marrakech, Morocco, March 13-15.
  • Joep J. M. Kierkels, Geert J. M. Van Botel, and Leo L. M. Vogten. “A Model-Based Objective Evaluation of Eye Movement Correction in EEG Recordings”, IEEE Transactions on biomedical engineering, vol. 53, No. 2, February 2006.
  • Muhammad Tahir Akhtar and Christopher J. James, "Focal Artifact Removal from Ongoing EEG – A Hybrid Approach Based on Spatially-Constrained ICA and Wavelet De-noising", Annual International Conference of the IEEE EMBS Minneapolis, 2009, Pp. 4027-4030.
  • N.P. Castellanos and V.A. Makarov, “Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis,” J. Neuroscience Methods, vol. 158, 2006, pp.300–312.
  • Vandana Roy et.all “Spatial and Transform Domain Filtering Method for Image De-noising: A Review” ,2013, International Journal of Modern Education and Computer Science.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp