Skip to main content

Advertisement

SpringerLink
Log in

Multifractality of decomposed EEG during imaginary and real visual-motor tracking

  • Original Paper
  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

We test the possible multifractal properties of dominant EEG frequency components, when a subject tracks a path on a map, either only by eyes (imaginary movement – IM) or by visual-motor tracking of discretely moving spot in regular (RM) and Brownian time-step (BM) (real tracking of moving spot). We check the hypotheses that the fractal properties of filtered EEG (1) change with respect to the law of spot movement; (2) differ among filtered EEG components and scalp sites; (3) differ among real and imaginary tracking. Sixteen right-handed subjects begin to perform IM, next – real spot tracking (RM and BM) following a moving spot on streets of a citymap displayed on a computer screen, by push forward/backward a joystick. Multichannel long-lasting EEG is band-pass filtered for theta, alpha, beta and gamma oscillations. The Wavelet-Transform-Modulus-Maxima-Method is applied to reveal multifractality [local fractal dimensions D max(h)] among task conditions, frequency bands and sites. Non-parametric statistical estimation of the fractal measures h D max is finally applied. Multifractality is established for all experimental conditions, EEG components and sites as follows among filtered components – anticorrelation (h Dmax < 0.5) in beta and gamma, and long-range correlation (h Dmax > 0.5) for theta and alpha oscillations; among tasks – for RM and BM, h Dmax differ significantly whereas IM resembles mostly RM; among sites – no significant difference for local fractal properties is established. The results suggest that for both imaginary and real visual-motor tracking a line, multifractal scaling, specific for lower and higher EEG oscillations, is a very stable intrinsic one for the activity of large brain areas. The external events (task conditions) insert weak effect on the scaling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Amaral LAN, Ivanov PCh, Aoyagi N, Hidaka I, Tomono S, Goldberger AI, Stanley HE, Yamamoto Y (2001) Behavioral-independent features of complex hearbeat dynamics. Phys Rev Lett 86(26):6026–6029

    Article  CAS  Google Scholar 

  • Basar E, Basar-Eroglu C, Demiralp T, Schurmann M (1995) Time and frequency analysis of the brain’s distributed gamma band system. IEEE Eng Med Biol 14:400–410

    Article  Google Scholar 

  • Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129–1159

    Article  PubMed  CAS  Google Scholar 

  • Chen Z, Hu K, Carpena P, Bernaola-Galvan P, Stanley HE, Ivanov PCh (2005) Effect of nonlinear filtres on detrended fluctuation analysis. Phys Rev E 71:011104

    Article  CAS  Google Scholar 

  • Dawson KA (2004) Temporal organization of the brain: neurocognitive mechanisms and clinical implication. Brain Cogn 54:75–94

    Article  PubMed  Google Scholar 

  • Elbert T, Ray WJ, Kowalik ZJ, Skinner JE, Graf KE, Birbaumer N (1994) Chaos and physiology: Deterministic chaos in excitable cell assemblies. Physiol Rev 74(1):1–47

    PubMed  CAS  Google Scholar 

  • Gilden DL, Thornton T, Mallon MW (1995) 1/f noise in human cognition. Science 267:1837–1839

    Article  PubMed  CAS  Google Scholar 

  • HuHu K, Ivanov PCh, Chen Z, Carpena P, Stanley HE (2001) Effect of trend on detrended fluctuation analysis. Phys Rev E 64:0111141–19

    Google Scholar 

  • Hyvaerinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New York, pp 211–219

    Google Scholar 

  • Ivanov PCh, Nunes Amaral LA, Goldberger AL, Havlin S, Rosenblum MG, Struzik ZR, Stanley HE (1999) Multifractality in human heartbeat dynamics. Nature 399:461–465

    Article  PubMed  CAS  Google Scholar 

  • Ivanov PCh, Nunes Amaral LA, Goldberger AL, Havlin S, Rosenblum MG, Stanley HE, Struzik ZR (2001) From 1/f noise to multifractal cascades in heartbeat dynamics. Chaos 11(3):641–652

    Article  PubMed  Google Scholar 

  • Jeong J, Joung M, Kim S (1998) Quantification of emotion by nonlinear analysis of the chaotic dynamics of electroencephalograms during perception of 1/f music. Biol Cybern 78:217–255

    Article  PubMed  CAS  Google Scholar 

  • Jung T-P, Makeig S, McKeown MJ, Bell AJ, Lee T-W, Sejnowski TJ (2001) Imaging brain dynamics using independent component analysis. Proc IEEE 89 (7):1107–1122

    Article  Google Scholar 

  • Klimesh W, Schimke H, Schwaiger J (1994) Episodic and semantic memory: an analysis in theta and alpha band. Electroencephalogr Clin Neurophysiol 91:428–441

    Article  Google Scholar 

  • Kosslyn, SM, Ganis G, Thompson WL (2001) Neural foundations of imagery. Nat Rev Neurosci 2:635–642

    Article  PubMed  CAS  Google Scholar 

  • MATLAB v. 5.3, The MathWorks Inc (1998)

  • Muzy JF, Bacry E, Arneodo A (1994) The multifractal formalism revisited with wavelets. Int J Bifurc at Chaos 4(2):245–302

    Article  Google Scholar 

  • Peng C-K, Havlin S, Stanley HE, Goldberger AL (1995) Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5:82–87

    Article  PubMed  CAS  Google Scholar 

  • Popivanov D, Mineva A, Krekule I (1999) EEG patterns in theta and gamma frequency range and their probable relation to human voluntary movement organization. Neurosci Lett, 267:5–8

    Article  CAS  Google Scholar 

  • Popivanov D, Janyan A, Andonova E, Stamenov M (2003) Common dynamic properties of biosignal during cognition: self-similarity and chaotic dynamics of both response times and EEG during movement imagery. Nonlinear Dyn, Psychol Life Sci 7(4):315–328

    Article  Google Scholar 

  • Popivanov D, Stomonyakov V, Dojnov P (2004) Multifractality measures of EEG: diagnostic distinction in healthy subjects and aphasics during cognitive task. In: Singh M, Radhakrishnan S (eds). Advances in medical diagnostic techniques and procedures. Anamaya Publishers, New Delhi, pp 42–47

    Google Scholar 

  • Popivanov D, Jivkova S, Stomonyakov V, Nicolova G (2005) Effect of independent component analysis on multifractality of EEG during visual-motor task. Signal Process 85:2112–2123

    Article  Google Scholar 

  • Poupard L, Sartene R, Wallet J-C (2001) Scaling behavior in β-wave amplitude modulation and its relationship to alertness. Biol Cybern 85:19–26

    Article  PubMed  CAS  Google Scholar 

  • Wilson SM, Saygin AP, Sereno MI, Iacoboni M (2004) Listening to speech activates motor areas involved in speech production. Nat Neurosci June 6:S1097–S6256

    Google Scholar 

  • Yordanova J, Kolev V, Demiralp T (1997) The phase-locking of auditory gamma band responses in humans is sensitive to task processing. Neuroreport 8:3999–4004

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Physiology, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria

    D. Popivanov, V. Stomonyakov, S. Jivkova, P. Dojnov & S. Jivkov

  2. New Bulgarian University, 1618, Sofia, Bulgaria

    D. Popivanov, E. Christova & S. Kosev

  3. Center for Biomedical Engineering, Bulgarian Academy of Science, 1113, Sofia, Bulgaria

    Z. Minchev

Authors
  1. D. Popivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Stomonyakov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Z. Minchev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. Jivkova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. P. Dojnov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Jivkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. E. Christova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. S. Kosev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Popivanov.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Popivanov, D., Stomonyakov, V., Minchev, Z. et al. Multifractality of decomposed EEG during imaginary and real visual-motor tracking. Biol Cybern 94, 149–156 (2006). https://doi.org/10.1007/s00422-005-0037-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00422-005-0037-5

Keywords

Search

Navigation