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Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations

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  • Bozhokin, S.V.
  • Suslova, I.B.

Abstract

In this paper we present a novel technique of studying EEG signals taking into account their essential nonstationarity. The bursts of activity in EEG rhythm are modeled as a superposition of specially designed elementary signals against the background of a real EEG record at rest. To calculate the time variation of quantitative characteristics of EEG patterns we propose the algorithm based on continuous wavelet transform (CWT) followed by the analysis of spectral integral dynamics in a given frequency range. We introduce new quantitative parameters to describe the dynamics of spectral properties both for each burst of brain activity and for their ensemble. Based on the given model we have identified the appearance and disappearance of patterns in EEG rhythm. The problem of non-stationary correlation of different EEG channels is solved. The use of the techniques for analyzing and classifying transient processes related to the activity of human central nervous system is also discussed.

Suggested Citation

  • Bozhokin, S.V. & Suslova, I.B., 2015. "Wavelet-based analysis of spectral rearrangements of EEG patterns and of non-stationary correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 151-160.
  • Handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:151-160
    DOI: 10.1016/j.physa.2014.11.026
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    as
    1. Arecchi, F.T, 2004. "Chaotic neuron dynamics, synchronization and feature binding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 218-237.
    2. Schönwald, Suzana V. & Gerhardt, Günther J.L. & de Santa-Helena, Emerson L. & Chaves, Márcia L.F., 2003. "Characteristics of human EEG sleep spindles assessed by Gabor transform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 327(1), pages 180-184.
    3. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    4. Stratimirović, Dj. & Milošević, S. & Blesić, S. & Ljubisavljević, M., 2007. "Wavelet transform analysis of time series generated by the stimulated neuronal activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(2), pages 699-706.
    5. Timashev, Serge F. & Panischev, Oleg Yu. & Polyakov, Yuriy S. & Demin, Sergey A. & Kaplan, Alexander Ya., 2012. "Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1179-1194.
    6. Paluš, M. & Dvořak, I. & David, I., 1992. "Spatio-temporal dynamics of human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 185(1), pages 433-438.
    7. Pereyra, M.E. & Lamberti, P.W. & Rosso, O.A., 2007. "Wavelet Jensen–Shannon divergence as a tool for studying the dynamics of frequency band components in EEG epileptic seizures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 122-132.
    8. Rosso, O.A. & Martin, M.T. & Plastino, A., 2005. "Evidence of self-organization in brain electrical activity using wavelet-based informational tools," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 444-464.
    9. Wang, Chun-mei & Zhang, Chong-ming & Zou, Jun-zhong & Zhang, Jian, 2012. "Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman–Pearson criteria and a support vector machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1602-1609.
    10. Zhang, J. & Yang, X.C. & Luo, L. & Shao, J. & Zhang, C. & Ma, J. & Wang, G.F. & Liu, Y. & Peng, C.-K. & Fang, J., 2009. "Assessing severity of obstructive sleep apnea by fractal dimension sequence analysis of sleep EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4407-4414.
    11. Dutta, Srimonti & Ghosh, Dipak & Samanta, Shukla & Dey, Santanu, 2014. "Multifractal parameters as an indication of different physiological and pathological states of the human brain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 155-163.
    12. Ishizaki, Ryuji & Shinba, Toshikazu & Mugishima, Go & Haraguchi, Hikaru & Inoue, Masayoshi, 2008. "Time-series analysis of sleep–wake stage of rat EEG using time-dependent pattern entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3145-3154.
    13. Abdulla, Waleed & Wong, Lisa, 2011. "Neonatal EEG signal characteristics using time frequency analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1096-1110.
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