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• W2885676273 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." @default.
• W2885676273 created "2018-08-22" @default.
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• W2885676273 date "2018-08-12" @default.
• W2885676273 modified "2023-09-24" @default.
• W2885676273 title "Non-linear Analysis of Time Series Generated from the Freeman K-Set Model" @default.
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• W2885676273 doi "https://doi.org/10.1007/978-981-13-0617-4_21" @default.
• W2885676273 hasPublicationYear "2018" @default.
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