  Purpose: The understanding of brain activity, and in particular events such
as epileptic seizures, lies on the characterisation of the dynamics of the
neural networks. The theory of non-linear dynamics provides signal analysis
techniques which may give new information on the behaviour of such networks.
Methods: We calculated correlation dimension maps for 19-channel EEG data from
3 patients with a total of 7 absence seizures. The signals were analysed
before, during and after the seizures. Phase randomised surrogate data was used
to test chaos. Results: In the seizures of two patients we could distinguish
two dynamical regions on the cerebral cortex, one that seemed to exhibit chaos
whereas the other seemed to exhibit noise. The pattern shown is essentially the
same for seizures triggered by hyperventilation, but differ for seizures
triggered by light flashes. The chaotic dynamics that one seems to observe is
determined by a small number of variables and has low complexity. On the other
hand, in the seizures of another patient no chaotic region was found. Before
and during the seizures no chaos was found either, in all cases. Conclusions:
The application of non-linear signal analysis revealed the existence of
differences in the spatial dynamics associated to absence seizures. This may
contribute to the understanding of those seizures and be of assistance in
clinical diagnosis.
