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Temporal lobe epilepsy has been known to be associated with buildup of 4-7 Hz activity preceded by attenuation near the seizure focus. Using a wavelet based algorithm, we recently showed that for the patients with temporal lobe epilepsy, frequency flow on the scalp EEG builds up to 5-12 Hz range just prior to and during the initial stages of the seizure. Here we present frequency flow analysis on EEG of patients with extra-temporal partial epilepsy and patients with idiopathic generalized epilepsy (IGE) to investigate any characteristic frequency flow patterns in these patients. We found that frequency flow in these patients also stays sustained in the 5-12 Hz range for longer periods of time just prior to and during the initial stages of the seizure compared to their respective baseline interictal recordings. The 5-12 Hz frequency flow was seen uniformly in all the channels in patients with IGE although it was seen most prominently adjacent to the seizure focus and to a lesser extent in other channels in patients with partial epilepsy., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Epilepsy affects 1%-2% of the general population, almost 50% of whom are medically refractory and poses a huge burden. A device can possibly be designed for responsive brain stimulation of the epileptogenic focus for seizure control but optimal seizure detection patterns and stimulus parameters are not yet determined. To explore the possibility of identifying a signature prior to the seizure, we studied the dynamic evolution of frequency from electroencephalography (EEG) of three patients with partial epilepsy using the method of frequency flow. We found that prior to seizure, the frequency flow dynamics exhibit a buildup of 5-12 Hz activity which remains high during the initial stage of the seizure with a characteristic time constant and may be used for seizure detection and/or anticipation. We conclude that dynamics of frequency flow in the 5-12 Hz range can be a potential marker for the detection and/or the anticipation of the seizure in temporal lobe epilepsy.

A study of the effect of time scales in brain dynamics on the unfolding of the attractors in the phase space, reconstructed by a time delay embedding of the EEG signal, was carried out. Applying the techniques of nonlinear time series analysis, the unfolding rate of the system attractor was determined by analyzing the variation of the correlation dimension parameter and subjecting it to a bi-parametric fit. The behavior of the parameter, which measures the rate of unfolding, was monitored for varying time scales in two cases: (a) normal eyes closed condition and (b) the pathological case of epilepsy. Significant results were obtained.

We propose to show in this paper, that the time series obtained from biological systems such as human brain are invariably nonstationary because of different time scales involved in the dynamical process. This makes the invariant parameters time dependent. We made a global analysis of the EEG data obtained from the eight locations on the skull space and studied simultaneously the dynamical characteristics from various parts of the brain. We have proved that the dynamical parameters are sensitive to the time scales and hence in the study of brain one must identify all relevant time scales involved in the process to get an insight in the working of brain.

It is shown that variation of Kolmogorov entropy is significantly sensitive to the choice of time lag during epileptic conditions. This clearly indicates that the human brain dynamics consist of different time scales and they are very crucial in the study of various disorders of brain from a nonlinear dynamical point of view.