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Usefulness of non‐linear EEG analysis
Author(s) -
Micheloyannis S.,
Flitzanis N.,
Papanikolaou E.,
Bourkas M.,
Terzakis D.,
Arvanitis S.,
Stam C. J.
Publication year - 1998
Publication title -
acta neurologica scandinavica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.967
H-Index - 95
eISSN - 1600-0404
pISSN - 0001-6314
DOI - 10.1111/j.1600-0404.1998.tb00603.x
Subject(s) - nonlinear system , electroencephalography , lyapunov exponent , dimension (graph theory) , correlation dimension , mutual information , coherence (philosophical gambling strategy) , entropy (arrow of time) , pattern recognition (psychology) , embedding , computer science , spectral density , artificial intelligence , mathematics , fractal dimension , statistics , chaotic , psychology , mathematical analysis , neuroscience , physics , quantum mechanics , pure mathematics , fractal
Spectral analysis methods are useful for the evaluation of EEG signals. Nevertheless, they refer only to the frequency domain and ignore any potentially interesting phase information. Analytical methods based upon the theory of nonlinear dynamics provides this and additional information. We used both methods to evaluate the EEG signals of volunteers performing two distinct mental arithmetic tasks. We extracted the power spectrum, the coherence and nonlinear parameters (dimension, the first Lyapunov exponent, the Kolmogorov entropy, the mutual dimension and the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dimension differed from that of the surrogates, indicating nonlinearity, 2) there were differences between the two arithmetic tasks, and 3) the spectral and nonlinear methods differ in terms of the information they provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of development.