Cognitive Neurodynamics

, Volume 6, Issue 1, pp 107–113 | Cite as

A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks

  • Stavros I. Dimitriadis
  • Nikolaos A. Laskaris
  • Vasso Tsirka
  • Sofia Erimaki
  • Michael Vourkas
  • Sifis Micheloyannis
  • Spiros Fotopoulos
Brief Communication

Abstract

Symbolic dynamics is a powerful tool for studying complex dynamical systems. So far many techniques of this kind have been proposed as a means to analyze brain dynamics, but most of them are restricted to single-sensor measurements. Analyzing the dynamics in a channel-wise fashion is an invalid approach for multisite encephalographic recordings, since it ignores any pattern of coordinated activity that might emerge from the coherent activation of distinct brain areas. We suggest, here, the use of neural-gas algorithm (Martinez et al. in IEEE Trans Neural Netw 4:558–569, 1993) for encoding brain activity spatiotemporal dynamics in the form of a symbolic timeseries. A codebook of k prototypes, best representing the instantaneous multichannel data, is first designed. Each pattern of activity is then assigned to the most similar code vector. The symbolic timeseries derived in this way is mapped to a network, the topology of which encapsulates the most important phase transitions of the underlying dynamical system. Finally, global efficiency is used to characterize the obtained topology. We demonstrate the approach by applying it to EEG-data recorded from subjects while performing mental calculations. By working in a contrastive-fashion, and focusing in the phase aspects of the signals, we show that the underlying dynamics differ significantly in their symbolic representations.

Keywords

Symbolic dynamics Multichannel EEG Transitions Math tasks 

Supplementary material

11571_2011_9186_MOESM1_ESM.doc (132 kb)
Supplementary material 1 (DOC 132 kb)

References

  1. Abásolo D, Hornero R, Gómez C, García M, López M (2006) Analysis of EEG background activity in Alzheimer’s disease patients with Lempel–Ziv complexity and central tendency measure. Med Eng Phy 28:315–322Google Scholar
  2. Amigó JM, Zambrano S, Sanjuán MAF (2010) Permutation complexity of spatiotemporal dynamics. EPL. doi:10007/10.1209/0295-5075/90/10007
  3. Baldi P, Brunak S (1998) Bioinformatics: the machine learning approach, 2nd edn. MIT Press, CambridgeGoogle Scholar
  4. Banerjee A, Tognoli E, Assisi C, Kelso S, Jirsa V (2008) Mode level cognitive subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies. Neuro Image 42:663–674PubMedGoogle Scholar
  5. Bozas K, Dimitriadis SI, Laskaris NA, Tzelepi A (2010) A novel single-trial analysis scheme for characterizing the presaccadic brain activity based on a SON representation paper presented at the 20th ICANN 2010, Thessaloniki, Greece, 15–18 Sep 2010Google Scholar
  6. Cohen L (1995) Time-frequency analysis: theory and applications, Chap. 2. Prentice-Hall, Upper Saddle River, p 30Google Scholar
  7. Daw CS, Finney CEA, Tracy ER (2001) Symbolic analysis of experimental data. Review of Scientific InstrumentsGoogle Scholar
  8. Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S (2010a) Tracking brain dynamics via time-dependent network analysis. J Neurosci Methods 193:145–155PubMedCrossRefGoogle Scholar
  9. Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S, Fotopoulos S (2010b) What does delta band tell us about cognitive processes: a mental calculation study. Neurosci Lett 483:11–15PubMedCrossRefGoogle Scholar
  10. Duch W, Dobosz K (2011) Visualization for understanding of neurodynamical systems. Cogn Neurodyn 5:145–160CrossRefGoogle Scholar
  11. Gao J, Hu J, Tung W (2011) Complexity measures of brain wave dynamics. Cogn Neurodyn 5:171–182CrossRefGoogle Scholar
  12. Hadamard J (1898) Les surfaces à courbures opposées et leurs lignes géodésiques. J Math Pures et Appl 4:27–73Google Scholar
  13. Laskaris N, Fotopoulos S, Ioannides AA (2004) Mining information from event related recordings. IEEE Signal Process Mag 21:66–77CrossRefGoogle Scholar
  14. Laskaris N, Kosmidis EK, Vucinic D, Homma R (2008) Understanding and characterizing olfactory responses. IEEE Eng Med Biol 27:69–79CrossRefGoogle Scholar
  15. Latora V, Marchiori M (2001) Efficient behaviour of small-world networks. Phys Rev Lett 87:198701PubMedCrossRefGoogle Scholar
  16. Martinez T, Berkovich S, Schulten K (1993) Neural-gas network for vector quantization and its application to time-series prediction. IEEE Trans Neural Netw 4:558–569CrossRefGoogle Scholar
  17. Marwan N, Romano MC, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Reports 438:237–329CrossRefGoogle Scholar
  18. Micheloyannis S, Sakkalis V, Vourkas M, Stam CJ, Simos PG (2005) Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity. Neurosci Lett 373:212–217PubMedCrossRefGoogle Scholar
  19. Ouyang G, Dang C, Richards DA, Li X (2010) Ordinal pattern based similarity analysis for EEG recordings. Clin Neurophysiol 121:694–703PubMedCrossRefGoogle Scholar
  20. Pascual-Marqui RD, Michel CM, Lehmann D (1995) Segmentation of brain electrical activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665PubMedCrossRefGoogle Scholar
  21. Sauseng P, Klimesch W (2008) What does phase information of oscillatory brain activity tell us about cognitive processes? Neurosci Biobehav Rev 32:1001–1013PubMedCrossRefGoogle Scholar
  22. Seneta E (1981) Non-negative matrices and markov chains, 2nd edn. Springer, New YorkGoogle Scholar
  23. Shannon CE, Weaver W (1998) The mathematical theory of communication reprinted. University of Illinois Press, UrbanaGoogle Scholar
  24. Sinatra R, Condorelli D, Latora V (2010) Networks of motifs from sequences of symbols. Phys Rev Lett 105:178702Google Scholar
  25. Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brain web: phase synchronization and large-scale integration. Nat Rev Neurosci 2:229–239PubMedCrossRefGoogle Scholar
  26. Zanin M (2008) Forbidden patterns in financial time series. Chaos 18:013119Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Stavros I. Dimitriadis
    • 1
    • 2
  • Nikolaos A. Laskaris
    • 2
  • Vasso Tsirka
    • 3
  • Sofia Erimaki
    • 3
  • Michael Vourkas
    • 4
  • Sifis Micheloyannis
    • 3
  • Spiros Fotopoulos
    • 1
  1. 1.Electronics Laboratory, Department of PhysicsUniversity of PatrasPatrasGreece
  2. 2.Artificial Intelligence and Information Analysis Laboratory, Department of InformaticsAristotle UniversityThessalonikiGreece
  3. 3.Medical Division (Laboratory L.Widén)University of CreteIraklion, CreteGreece
  4. 4.Technical High School of Crete, EstavromenosIraklion, CreteGreece

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