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Biol Cybern ; 94(2): 149-56, 2006 Feb.
Article in English | MEDLINE | ID: mdl-16341722

ABSTRACT

We test the possible multifractal properties of dominant EEG frequency components, when a subject tracks a path on a map, either only by eyes (imaginary movement - IM) or by visual-motor tracking of discretely moving spot in regular (RM) and Brownian time-step (BM) (real tracking of moving spot). We check the hypotheses that the fractal properties of filtered EEG (1) change with respect to the law of spot movement; (2) differ among filtered EEG components and scalp sites; (3) differ among real and imaginary tracking. Sixteen right-handed subjects begin to perform IM, next--real spot tracking (RM and BM) following a moving spot on streets of a citymap displayed on a computer screen, by push forward/backward a joystick. Multichannel long-lasting EEG is band-pass filtered for theta, alpha, beta and gamma oscillations. The Wavelet-Transform-Modulus-Maxima-Method is applied to reveal multifractality [local fractal dimensions Dmax(h)] among task conditions, frequency bands and sites. Non-parametric statistical estimation of the fractal measures h (Dmax) is finally applied. Multifractality is established for all experimental conditions, EEG components and sites as follows among filtered components - anticorrelation (h(Dmax) < 0.5) in beta and gamma, and long-range correlation (h(Dmax) > 0.5) for theta and alpha oscillations; among tasks--for RM and BM, h (Dmax) differ significantly whereas IM resembles mostly RM; among sites--no significant difference for local fractal properties is established. The results suggest that for both imaginary and real visual-motor tracking a line, multifractal scaling, specific for lower and higher EEG oscillations, is a very stable intrinsic one for the activity of large brain areas. The external events (task conditions) insert weak effect on the scaling.


Subject(s)
Brain Mapping , Cerebral Cortex/physiology , Electroencephalography , Evoked Potentials, Visual/physiology , Imagination/physiology , Algorithms , Confidence Intervals , Electrodes , Humans , Signal Processing, Computer-Assisted
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