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.

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.

nullSenapati, K. and Routray, A. (2011) Comparison of ICA and WT with S-transform based method for removal of ocular artifact from EEG signals.

References

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[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.

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[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

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[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

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[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

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[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.

[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.