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Non-linear Analysis of Time Series Generated from the Freeman K-Set Model

  • F. Anitta
  • R. Sunitha
  • N. Pradhan
  • A. Sreedevi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

Brain signals such as EEG and MEG are the only available dynamical measures of functional status of the brain. Over past several years EEG has been found to have nonlinear and chaotic properties. The nonlinear dynamical measures have been linked to brain functioning including the most complex cognitive behavior of man. Our study focuses on showing evidence of nonlinear chaotic behavior of simulated EEG. We have simulated the EEG at the mesoscopic level by using the biologically realistic Freeman K-sets. Here the behavior of the time series at every level of the olfactory system as modeled in the Freeman-KIII set is obtained by solving a set of second-order differential equations using Euler method in MATLAB. The generated low-dimensional- and high-dimensional time series is subjected to a nonlinear analysis using Higuchi fractal dimension, Lyapunov exponent, and Detrended Fluctuation analysis to validate the chaotic behavior. The study indirectly points to suitability of Freeman model for large-scale brain simulation.

Keywords

Freeman K-set model Higuchi fractal dimension Lyapunov exponent Detrended fluctuation analysis 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • F. Anitta
    • 1
  • R. Sunitha
    • 1
  • N. Pradhan
    • 2
  • A. Sreedevi
    • 3
  1. 1.Department of Electronics and Communication Engineering, Amrita School of EngineeringBengaluru Amrita Vishwa VidyapeethamBengaluruIndia
  2. 2.Department of PsychopharmacologyNational Institute of Mental Health and Neurosciences (NIMHANS)BengaluruIndia
  3. 3.Department of Electrical and Electronics EngineeringR. V. College of EngineeringBengaluruIndia

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