A Unique Approach of Noise Elimination from Electroencephalography Signals between Normal and Meditation State

Authors

Electrical Engineering Department, VJTI Mumbai, India

Abstract

In this paper, unique approach is presented for the electroencephalography (EEG) signals analysis. This is based on Eigen values distribution of a matrix which is called as scaled Hankel matrix. This gives us a way to find out the number of Eigen values essential for noise reduction and extraction of signal in singular spectrum analysis. This paper gives us an approach to classify the EEG signals between normal condition (Controlled) and meditation condition, the extraction of various patterns, the EEG signal filtering and the noise removal from the signals. Different parameters are used as features for classification during subject’s normal EEG segments and at the time of practicing Meditation. The results showed positive approach for noise removal in both EEG signals.

Keywords


1.     Guerrero-Mosquera, C., Trigueros, A.M. and Navia-Vazquez, A., Eeg signal processing for epilepsy, in Epilepsy-histological, electroencephalographic and psychological aspects. 2012, InTech.

2.     Khatwani, P. and Tiwari, A., "A survey on different noise removal techniques of eeg signals", International Journal of Advanced Research in Computer and Communication Engineering,  Vol. 2, No. 2, (2013), 1091-1095.

3.     Walters-Williams, J. and Li, Y., "A new approach to denoising eeg signals-merger of translation invariant wavelet and ica", International Journal of Biometrics and Bioinformatics,  Vol. 5, No. 2, (2011), 130-148.

4.     Vigário, R., Jousmäki, V., Hämäläinen, M., Hari, R. and Oja, E., "Independent component analysis for identification of artifacts in magnetoencephalographic recordings", in Advances in neural information processing systems., (1998), 229-235.

5.     Inuso, G., La Foresta, F., Mammone, N. and Morabito, F.C., "Wavelet-ica methodology for efficient artifact removal from electroencephalographic recordings", in Neural Networks, 2007. IJCNN 2007. International Joint Conference on, IEEE., (2007), 1524-1529.

6.     Kang, D. and Zhizeng, L., "A method of denoising multi-channel eeg signals fast based on pca and debss algorithm", in Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on, IEEE. Vol. 3, (2012), 322-326.

7.     Babu, P.A. and Prasad, K., "Removal of ocular artifacts from eeg signals using adaptive threshold pca and wavelet transforms", in Communication Systems and Network Technologies (CSNT), 2011 International Conference on, IEEE., (2011), 572-575.

8.     Karhunen, J. and Joutsensalo, J., "Generalizations of principal component analysis, optimization problems, and neural networks", Neural Networks,  Vol. 8, No. 4, (1995), 549-562.

9.     Kumar, P.S., Arumuganathan, R., Sivakumar, K. and Vimal, C., "Removal of ocular artifacts in the eeg through wavelet transform without using an eog reference channel", International Journal Open Problems Computer and Mathematics,  Vol. 1, No. 3, (2008), 188-200.

10.   Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P. and Elger, C.E., "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state", Physical Review E,  Vol. 64, No. 6, (2001), 061907.

11.   Kantz, H. and Schreiber, T., "Nonlinear time series analysis, Cambridge university press,  Vol. 7,  (2004).

12.   Lehnertz, K., Elger, C., Arnhold, J. and Grassberger, P., "Chaos in brain", in University of Bonn, Germany., (1999 of Conference).

13.   Rukhin, A.L., Analysis of time series structure ssa and related techniques. 2002, Taylor & Francis.

14.   Golyandina, N. and Zhigljavsky, A., "Singular spectrum analysis for time series, Springer Science & Business Media,  (2013).

15.   Rodriguez-Aragon, L.J. and Zhigljavsky, A., "Singular spectrum analysis for image processing", Statistics and Its Interface,  Vol. 3, No. 3, (2010), 419-426.

16.   Liew, A.W.-C., Tang, V.T.-Y. and Yan, H., "Periodicity analysis of DNA microarray gene expression time series profiles in mouse segmentation clock data", Statistics and Its Interface,  Vol. 3, No. 3, (2010), 413-418.

17.   Celka, P. and Colditz, P., "A computer-aided detection of eeg seizures in infants: A singular-spectrum approach and performance comparison", IEEE Transactions on Biomedical Engineering,  Vol. 49, No. 5, (2002), 455-462.

18.   Welvaert, M. and Rosseel, Y., "On the definition of signal-to-noise ratio and contrast-to-noise ratio for fmri data", PloS One,  Vol. 8, No. 11, (2013), e77089.

19.   Kannathal, N., Choo, M.L., Acharya, U.R. and Sadasivan, P., "Entropies for detectionof epilepsy in eeg", Computer Methods and Programs in Biomedicine,  Vol. 80, No. 3, (2005), 187-194.