Nonlinear Dynamics

, Volume 83, Issue 4, pp 1909–1917 | Cite as

Analysis of electrocorticogram in epilepsy patients in terms of criticality

  • Jiaqing Yan
  • Yinghua Wang
  • Gaoxiang Ouyang
  • Tao Yu
  • Yongjie Li
  • Attila Sik
  • Xiaoli LiEmail author
Original Paper


Self-organized criticality is being considered as a potential organization of the brain. In this study, major features of critical systems were applied to investigate the power-law distributions of human electrocorticogram (ECoG) data, with the aim of determining whether the critical regime could be applied to reveal the underling change of epileptic seizure generation. Multiple brain region ECoG signal was recorded from three epilepsy patients, including inter-ictal, pre-ictal, ictal and postictal stages. The Hurst exponent (H) parameter from the power-law analysis was calculated based on the ECoG signal spectrum estimated using a harmonic wavelet transform-based power-law analysis method. The changes in H at normal, inter-ictal spike, ictal stages were discussed in the framework of criticality theory. The H parameter could describe the dynamics of seizure generation. When inter-ictal spike occurred, H became larger than 0.5, suggesting that the underlying system changed from non-persistent to persistent dynamics. However, when seizure occurred, the ECoG dynamics changed into a state that H cannot indicate. The power-law analysis with Hurst exponent can be used to describe the generation of epileptic seizure. This analytical method provides a new insight to the understanding of the generation mechanism of epileptic seizures in terms of criticality, which could be used to design a prediction and/or detection method for closed-loop control of epilepsy.


Epilepsy Electrocorticogram  Power law Criticality Hurst exponent 



This research was supported by the National Natural Science Foundation of China (Nos. 61273063, 81230023), the NSFC–RS (No. 61311130139), and The commercialization of research fund supported by Beijing Municipal Commission of Education.

Author contributions X.L. L, T. Y and A. S designed this study. J.Q. Y and Y.H. W analyzed data from the experiments and prepared figures. J.Q. Y, Y. H. W, A. S, and X.L. L wrote the manuscript. J.Q Y and Y.H. W equally contributed to this study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. 1.
    Worrell, G.A., Cranstoun, S.D., Echauz, J., Litt, B.: Evidence for self-organized criticality in human epileptic hippocampus. Neuroreport 13(16), 2017–2021 (2002)CrossRefGoogle Scholar
  2. 2.
    Shepherd, G.M., Erulkar, S.D.: Centenary of the synapse: from Sherrington to the molecular biology of the synapse and beyond. Trends Neurosci. 20(9), 385–392 (1997)CrossRefGoogle Scholar
  3. 3.
    Gao, J., Hu, J., Tung, W.-W.: Entropy measures for biological signal analyses. Nonlinear Dyn. 68(3), 431–444 (2012)CrossRefMathSciNetzbMATHGoogle Scholar
  4. 4.
    Rubchinsky, L.L., Park, C., Worth, R.M.: Intermittent neural synchronization in Parkinson’s disease. Nonlinear Dyn. 68(3), 329–346 (2012)CrossRefGoogle Scholar
  5. 5.
    Bannister, K., Lee, Y.S., Goncalves, L., Porreca, F., Lai, J., Dickenson, A.H.: Neuropathic plasticity in the opioid and non-opioid actions of dynorphin. A fragments and their interactions with bradykinin B2 receptors on neuronal activity in the rat spinal cord. Neuropharmacology 85, 375–383 (2014)CrossRefGoogle Scholar
  6. 6.
    Kapiris, P.G., Polygiannakis, J., Li, X., Yao, X., Eftaxias, K.A.: Similarities in precursory features in seismic shocks and epileptic seizures. Europhys Lett. 69(4), 657–663 (2005)CrossRefGoogle Scholar
  7. 7.
    Shew, W.L., Plenz, D.: The functional benefits of criticality in the cortex. Neurosci. 19(1), 88–100 (2013)Google Scholar
  8. 8.
    Linkenkaer-Hansen, K., Nikouline, V.V., Palva, J.M., Ilmoniemi, R.J.: Long-range temporal correlations and scaling behavior in human brain oscillations. J. Neurosci. 21(4), 1370–1377 (2001)Google Scholar
  9. 9.
    Garcia, S.B., Stopper, H., Kannen, V.: The contribution of neuronal-glial-endothelial-epithelial interactions to colon carcinogenesis. Cell. Mol. Life Sci. 71(17), 3191–3197 (2014)CrossRefGoogle Scholar
  10. 10.
    Maxim, V., Sendur, L., Fadili, J., Suckling, J., Gould, R., Howard, R., Bullmore, E.: Fractional Gaussian noise, functional MRI and Alzheimer’s disease. Neuroimage 25(1), 141–158 (2005)CrossRefGoogle Scholar
  11. 11.
    Dan, C., Lizhe, W., Shuaiting, W., Muzhou, X., von Laszewski, G., Xiaoli, L.: Enabling energy-efficient analysis of massive neural signals using GPGPU. In: 2010 IEEE/ACM International Conference on Green Computing and Communications (GreenCom) and International Conference on Cyber, Physical and Social Computing (CPSCom), pp. 147–154 (2010)Google Scholar
  12. 12.
    Weiss, R., Bartok, O., Mezan, S., Malka, Y., Kadener, S.: Synergistic interactions between the molecular and neuronal circadian networks drive robust behavioral circadian rhythms in Drosophila melanogaster. PLoS Genet. 10(4), e1004252 (2014). doi: 10.1371/journal.pgen.1004252 CrossRefGoogle Scholar
  13. 13.
    Li, X., Polygiannakis, J., Kapiris, P., Peratzakis, A., Eftaxias, K., Yao, X.: Fractal spectral analysis of pre-epileptic seizures in terms of criticality. J. Neural Eng. 2(2), 11–16 (2005)CrossRefGoogle Scholar
  14. 14.
    Kotani, K., Yamaguchi, I., Yoshida, L., Jimbo, Y., Ermentrout, G.B.: Population dynamics of the modified theta model: macroscopic phase reduction and bifurcation analysis link microscopic neuronal interactions to macroscopic gamma oscillation. J. R. Soc. Interface 11(95), 20140058 (2014). doi: 10.1098/rsif.2014.0058 CrossRefGoogle Scholar
  15. 15.
    Mandelbr, Bb, Vanness, J.W.: Fractional Brownian motions fractional noises and applications. Siam Rev. 10(4), 422 (1968)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Heneghan, C., McDarby, G.: Establishing the relation between detrended fluctuation analysis and power spectral density analysis for stochastic processes. Phys. Rev. E. 62(5), 6103–6110 (2000)CrossRefGoogle Scholar
  17. 17.
    Janjarasjitt, S., Loparo, K.A.: Scale-invariant behavior of epileptic ECoG. J. Med. Biol. Eng. 34(6), 535–541 (2014)Google Scholar
  18. 18.
    Eftaxias, K., Minadakis, G., Potirakis, S.M., Balasis, G.: Dynamical analogy between epileptic seizures and seismogenic electromagnetic emissions by means of nonextensive statistical mechanics. Phys. A 392(3), 497–509 (2013)CrossRefGoogle Scholar
  19. 19.
    Samara, C., Poirot, O., Domenech-Estevez, E., Chrast, R.: Neuronal activity in the hub of extrasynaptic Schwann cell–axon interactions. Front. Cell. Neurosci. 7, 228 (2013). doi: 10.3389/fncel.2013.00228 CrossRefGoogle Scholar
  20. 20.
    Milton, J.G.: Neuronal avalanches, epileptic quakes and other transient forms of neurodynamics. Eur. J. Neurosci. 36(2), 2156–2163 (2012)CrossRefMathSciNetGoogle Scholar
  21. 21.
    Mitterauer, B.J.: Intuition in autistic savantism: a hypothetical model based on glial-neuronal interactions. Med. Hypotheses 81(6), 1083–1087 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Jiaqing Yan
    • 1
  • Yinghua Wang
    • 2
    • 3
  • Gaoxiang Ouyang
    • 2
    • 3
  • Tao Yu
    • 4
  • Yongjie Li
    • 4
  • Attila Sik
    • 5
  • Xiaoli Li
    • 2
    • 3
    Email author
  1. 1.Institute of Electrical EngineeringYanshan UniversityQinhuangdaoChina
  2. 2.State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  3. 3.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina
  4. 4.Beijing Institute of Functional NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
  5. 5.College of Medical and Dental SciencesUniversity of BirminghamBirminghamUK

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