IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v421y2015icp151-160.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114009777
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2014.11.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongquan Qu & Mengyu Zhang & Liping Pang, 2022. "Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment," Mathematics, MDPI, vol. 10(11), pages 1-14, May.
    2. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.
    3. Guo, Jia-Yi & Cai, Qing & An, Jian-Peng & Chen, Pei-Yin & Ma, Chao & Wan, Jun-He & Gao, Zhong-Ke, 2022. "A Transformer based neural network for emotion recognition and visualizations of crucial EEG channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lahmiri, Salim, 2018. "Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 378-385.
    2. Alencar, Adriano M. & da Silva, Diego Greatti Vaz & Oliveira, Carolina Beatriz & Vieira, André P. & Moriya, Henrique T. & Lorenzi-Filho, Geraldo, 2013. "Dynamics of snoring sounds and its connection with obstructive sleep apnea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 271-277.
    3. Lipovetsky, Stan, 2018. "Quantum paradigm of probability amplitude and complex utility in entangled discrete choice modeling," Journal of choice modelling, Elsevier, vol. 27(C), pages 62-73.
    4. Mark L Ioffe & Michael J Berry II, 2017. "The structured ‘low temperature’ phase of the retinal population code," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-31, October.
    5. Yasser Roudi & Sheila Nirenberg & Peter E Latham, 2009. "Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't," PLOS Computational Biology, Public Library of Science, vol. 5(5), pages 1-18, May.
    6. Maulana, Ardian & Situngkir, Hokky, 2015. "Korelasi Bebas-skala dalam Studi Geo-politik Pemilihan [Scale-free correlation within Geopolitics of Election Studies]," MPRA Paper 66351, University Library of Munich, Germany.
    7. Mulligan, Robert F., 2017. "The multifractal character of capacity utilization over the business cycle: An application of Hurst signature analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 147-152.
    8. Hideaki Shimazaki & Shun-ichi Amari & Emery N Brown & Sonja Grün, 2012. "State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data," PLOS Computational Biology, Public Library of Science, vol. 8(3), pages 1-27, March.
    9. Timothy R Lezon & Ivet Bahar, 2010. "Using Entropy Maximization to Understand the Determinants of Structural Dynamics beyond Native Contact Topology," PLOS Computational Biology, Public Library of Science, vol. 6(6), pages 1-12, June.
    10. Sacha Jennifer van Albada & Moritz Helias & Markus Diesmann, 2015. "Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-37, September.
    11. Sahar Gelfman & Quanli Wang & Yi-Fan Lu & Diana Hall & Christopher D Bostick & Ryan Dhindsa & Matt Halvorsen & K Melodi McSweeney & Ellese Cotterill & Tom Edinburgh & Michael A Beaumont & Wayne N Fran, 2018. "meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-20, October.
    12. Jason S Prentice & Olivier Marre & Mark L Ioffe & Adrianna R Loback & Gašper Tkačik & Michael J Berry II, 2016. "Error-Robust Modes of the Retinal Population Code," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-32, November.
    13. Montani, Fernando & Phoka, Elena & Portesi, Mariela & Schultz, Simon R., 2013. "Statistical modelling of higher-order correlations in pools of neural activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(14), pages 3066-3086.
    14. Richard R Stein & Debora S Marks & Chris Sander, 2015. "Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-22, July.
    15. Urs Köster & Jascha Sohl-Dickstein & Charles M Gray & Bruno A Olshausen, 2014. "Modeling Higher-Order Correlations within Cortical Microcolumns," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-12, July.
    16. Gabriel Baglietto & Guido Gigante & Paolo Del Giudice, 2017. "Density-based clustering: A ‘landscape view’ of multi-channel neural data for inference and dynamic complexity analysis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-25, April.
    17. Banerjee, Archi & Sanyal, Shankha & Roy, Souparno & Nag, Sayan & Sengupta, Ranjan & Ghosh, Dipak, 2021. "A novel study on perception–cognition scenario in music using deterministic and non-deterministic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 567(C).
    18. Porta Mana, PierGianLuca & Rostami, Vahid & Torre, Emiliano & Roudi, Yasser, 2018. "Maximum-entropy and representative samples of neuronal activity: a dilemma," OSF Preprints uz29n, Center for Open Science.
    19. Zhang, Qi & Li, Meizhu, 2022. "A betweenness structural entropy of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    20. Litak, Grzegorz & Polyakov, Yuriy S. & Timashev, Serge F. & Rusinek, Rafał, 2013. "Dynamics of stainless steel turning: Analysis by flicker-noise spectroscopy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6052-6063.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:421:y:2015:i:c:p:151-160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.