Dynamic character and age dependence of functional brain organization in attention

Dubrovinskaya, N. V.; Machinskaya, R. I.; Kulakovsky, Yu.V.

doi:10.1007/bf02462944

Cited by 3 publications

(1 citation statement)

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“…The electroencephalogram (EEG) re¯ects the activities of these networks and forms a complex spatio-temporal pattern (Lopes da Silva 1991;Gevins et al 1994;Dubrovinskaya et al 1997;Crone et al 1998). The current theory of nonlinear dynamics has provided a useful method of detecting the properties of brain functions.…”

Section: Introductionmentioning

confidence: 99%

Topographic analysis of dimension estimates of EEG and filtered rhythms in epileptic patients with complex partial seizures

Jing

Takigawa

2000

Biological Cybernetics

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Nonlinear dynamic properties were analyzed on the EEG and filtered rhythms recorded from healthy subjects and epileptic patients with complex partial seizures. Estimates of correlation dimensions of control EEG, interictal EEG and ictal EEG were calculated. The values were demonstrated on topograms. The delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-40 Hz) components were obtained and considered as signals from the cortex. Corresponding surrogate data was produced. Firstly, the influence of sampling parameters on the calculation was tested. The dimension estimates of the signals from the frontal, temporal, parietal and occipital regions were computed and compared with the results of surrogate data. In the control subjects, the estimates between the EEG and surrogate data did not differ (P > 0.05). The interictal EEG from the frontal region and occipital region, as well as its theta component from the frontal region, and temporal region, showed obviously low dimensions (P < 0.01). The ictal EEG exhibited significantly low-dimension estimates across the scalp. All filtered rhythms from the temporal region yielded lower results than those of the surrogate data (P < 0.01). The dimension estimates of the EEG and filtered components markedly changed when the neurological state varied. For each neurological state, the dimension estimates were not uniform among the EEG and frequency components. The signal with a different frequency range and in a different neurological state showed a different dimension estimate. Furthermore, the theta and alpha components demonstrated the same estimates not only within each neurological state, but also among the different states. These results indicate that the theta and alpha components may be caused by similar dynamic processes. We conclude that the brain function underlying the ictal EEG has a simple mechanism. Several heterogeneous dynamic systems play important roles in the generation of EEG.

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Section: Introductionmentioning

confidence: 99%