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 JBiSE  Vol.4 No.5 , May 2011
Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals
Abstract: Ocular artifacts are most unwanted disturbance in electroencephalograph (EEG) signals. These are characterized by high amplitude but have overlap-ping frequency band with the useful signal. Hence, it is difficult to remove the ocular artifacts by traditional filtering methods. This paper proposes a new approach of artifact removal using S-transform (ST). It provides an instantaneous time-frequency repre-sentation of a time-varying signal and generates high magnitude S-coefficients at the instances of abrupt changes in the signal. A threshold function has been defined in S-domain to detect the artifact zone in the signal. The artifact has been attenuated by a suitable multiplying factor. The major advantage of ST-fil- tering is that the artifacts may be removed within a narrow time-window, while preserving the frequency information at all other time points. It also preserves the absolutely referenced phase information of the signal after the removal of artifacts. Finally, a com-parative study with wavelet transform (WT) and in-dependent component analysis (ICA) demonstrates the effectiveness of the proposed approach.
Cite this paper: nullSenapati, K. and Routray, A. (2011) Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals. Journal of Biomedical Science and Engineering, 4, 341-351. doi: 10.4236/jbise.2011.45043.
References

[1]   Krishnaveni, V., Jayaraman, S., Aravind, S., Hariharasud- han, V. and Ramadoss, K. (2006) Automatic identification and removal of ocular artifacts from EEG using wavelet transform. Measurement Science Review, 6, 45-57.

[2]   Croft, R.J. and Barry, R.J. (2000) Removal of ocular artifact from the EEG: A review. Clinical Neurophysiology, 30, 5-19. doi:10.1016/S0987-7053(00)00055-1

[3]   Kandaswamy, A., Krishnaveni, V., Jayaraman, S., Malmurugan, N. and Ramadoss, K. (2005) Removal of ocular artifacts from EEG - A Survey. IETE Journal of Research, 52, 112-130.

[4]   Lagerlund, T.D., Sharbrough, F.W. and Busacker, N.E. (1997) Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Clinical Neurophysiology, 14, 73-82. doi:10.1097/00004691-199701000-00007

[5]   Jung, T., Makeig, S., Humphries, C., Lee, T., Mckeown, M.J., Iragui, V. and Sejnowski, T.J. (1998) Extended ICA removes artifacts from electroencephalographic recordings. Advances in Neural Information Processing Systems 10, MIT Press, Cambridge, 894-900.

[6]   Delorme, A., Makeig, S. and Sejnowski, T. (2001) Automatic artifact rejection for EEG data using high-order statistics and independent component analysis. Proceedings of the Third International ICA Conference, San Diego, CA, December 9-13.

[7]   LeVan, P., Urrestarrazu E. and Gotman, J. (2006) A system for automatic artifact removal in ictal scalp EEG based on independent component analysis and Bayesian classification. Clinical Neurophysiology, 117, 912-927. doi:10.1016/j.clinph.2005.12.013

[8]   Kumar, P.S., Arumuganathan, R., Sivakumar, K. and Vimal, C.A. (2008) Wavelet based statistical method for de-noising of ocular artifacts in EEG signals. International Journal of Computer Science and Network Security, 8, 87-92.

[9]   Stockwell, R.G., Mansinha, L. and Lowe, R.P. (1996) Localization of the complex spectrum: The S transform. IEEE Transactions on Signal Processing, 44, 998-1001. doi:10.1109/78.492555

[10]   Mansinha, L., Stockwell, R.G., Lowe, R.P., Eramian, M. and Schincariol, R.A. (1997) Local S-spectrum analysis of 1-D and 2-D data. Physics of the Earth and Planetary Interiors, 103, 329-336. doi:10.1016/S0031-9201(97)00047-2

[11]   Moreau, E. and Macchi, O. (1993) New self adaptive algorithms for source separation based on contrast functions. Proceeding IEEE Signal Processing Workshop on Higher Order Statistics, Lake Tahoe, June 1993, 215-219.

[12]   Amari, S., Cichocki A. and Yang, H. (1996) A new learning algorithm for blind signal separation. Advances in Neural Information Processing, 8, 757-763.

[13]   Amari, S. and Cichocki, A. (1998) Adaptive blind signal processing-neural network approaches. Proceedings of the IEEE, 86, 2026-2048. doi:10.1109/5.720251

[14]   Hyvarinen, A. (1997) Independent component analysis by minimization of mutual information (technical report). Helsinki University of Technology.

[15]   Lee, T.W., Girolami, M. and Sejnowski, T.J. (1999) Independent component analysis using an extended info- max algorithm for mix sub-gaussian and super-gaussian sources. Neural Computation, 11, 417-441. doi:10.1162/089976699300016719

[16]   Cichicki, A. and Amari, S. (2002) Adaptive blind signal and image processing: Learning algorithms and applications. John Wiley & Sons Ltd., New York. doi:10.1002/0470845899

[17]   Hyv?rinen, A., Karhunen, J. and Oja, E. (2001) Independent component analysis. John Wiley & Sons Ltd., New York.

[18]   Bell, A. and Sejnowski, T. (1995) Information-maximi- zation approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159. doi:10.1162/neco.1995.7.6.1129

[19]   Daubechies. I. (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36, 961-1005. doi:10.1109/18.57199

[20]   Mallat, S. (1999) A wavelet tour of signal processing. 2nd Edition, Academic Press, San Diego.

[21]   Rioul, O. and Vetterli, M. (1991) Wavelets and signal processing. IEEE Signal Processing Magazine, 14-38.

[22]   Chu, P.C. (1996) The S-transform for obtaining localized spectra. Marine Technology Society Journal, 29, 28-38.

[23]   Stockwell, R.G., Lowe, R.P. and Mansinha, L. (1996) Localized cross spectral analysis with “phase corrected” wavelets. The International Society for Optical Engineering (SPIE) Proceedings 2762, Orlando, 8-12 April 1996, 557-564.

[24]   Stockwell, R.G., Lowe, R.P. and Mansinha, L. (1997) Instantaneous wavevector analysis. The International Society for Optical Engineering (SPIE) Proceedings 3078, Orlando, 24 April 1997, 349-358.

[25]   Stockwell, R.G. (1999) S-transform analysis of gravity wave activity from a small scale network of airglow imagers. Ph.D. Thesis, University of Western Ontario, Ontario.

[26]   Livanos, G., Ranganathan, N. and Jiang, J. (2000) Heart sound analysis using the S-transform. IEEE Computers in Cardiology, 27, 587-590.

[27]   Varanini, M., De Paolis, G., Emdin, M., Macareta, A., Pola, S., Cipriani, M. and Marchesi, C. (1997) Spectral analysis of cardiovascular time series by the S-transform. IEEE Computers in Cardiology, 24, 383-386.

[28]   Eramian, M.G., Schincariol, R.A., Mansinha, L. and Stockwell, R.G. (1999) Generation of aquifer heterogeneity maps using two-dimensional spectral texture segmentation techniques. Mathematical Geology, 31, 327-348. doi:10.1023/A:1007578305616

[29]   McFadden, P.D., Cook, J.G. and Forster, L.M. (1999) Decomposition of gear vibration signals by the generalized S-transform. Mechanical Systems and Signal Processing, 13, 691-707. doi:10.1006/mssp.1999.1233

[30]   Ventosa, S., Simon, C., Schimmel, M., Da?obeitia, J.J. and Mànuel, A. (2008) The S-transform from a wavelet point of view. IEEE Transactions on Signal Processing, 56, 2771-2780. doi:10.1109/TSP.2008.917029

[31]   Stockwell, R.G. (2007) Why use the S-transform? AMS Pseudo-differential operators: Partial differential equations and time-frequency analysis, 52, 279-309.

 
 
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