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Nonlinear structures in electroencephalogram signals

Author

Listed:
  • Diambra, L.
  • Malta, C.P.
  • Capurro, A.
  • Fernández, J.

Abstract

We apply a nonlinear prediction algorithm to investigate the presence of nonlinear structure in electroencephalogram (EEG) recordings. The EEG signal could be modeled as a realization of a nonlinear model plus a residual noise (uncorrelated Gaussian noise). Using linear and nonlinear models we analyze the statistical nature of these residual noises in the case of epileptic patients and normal subjects. We found that the residual noise presents Gaussian distribution for epileptic patients if a nonlinear model is used whereas in the case of normal subjects the residual noise will exhibit a Gaussian distribution only if a linear model (autoregressive) is used. These results provide another evidence of the nonlinear character of the epileptic seizure recordings, while the normal EEG seems to be better described as linearly correlated noise.

Suggested Citation

  • Diambra, L. & Malta, C.P. & Capurro, A. & Fernández, J., 2001. "Nonlinear structures in electroencephalogram signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 300(3), pages 505-520.
    • Handle: RePEc:eee:phsmap:v:300:y:2001:i:3:p:505-520
    DOI: 10.1016/S0378-4371(01)00352-1
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    References listed on IDEAS

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    1. Capurro, A. & Diambra, L. & Lorenzo, D. & Macadar, O. & Martin, M.T. & Mostaccio, C. & Plastino, A. & Pérez, J. & Rofman, E. & Torres, M.E. & Velluti, J., 1999. "Human brain dynamics: the analysis of EEG signals with Tsallis information measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 265(1), pages 235-254.
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    Cited by:

    1. Dünki, R.M. & Dressel, M., 2006. "Statistics of biophysical signal characteristics and state specificity of the human EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 370(2), pages 632-650.

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