In: Handbook of Electroencephalography and Clinical Neurophysiology, Revised series, vol. Methods of analysis of brain electrical and magnetic signals. 2, Handbook of Electroencephalography and Clinical Neurophysiology, 1986, 335вЂ“384. Remond, (Eds.), Clinical Applications of Computer Analysis of EEG and other Neurophysiological Signals, Revised series, vol. American Mathematical Society Translations, 1959, 12: 199вЂ“246. Calculation of the amount of information about a random function contained in another such function. Beschrijvende statistiek voor gedragswetenschappen. Neurophysiol., 1990 (In press).Д®nde, H.W. The role of hippocampal commissures in the interhemispheric transfer of epileptiform afterdischarges in the rat: a study using linear and nonlinear regression analysis. Synchronization characteristics of paroxysmal EEG activity. Coupling between cortical potentials from different areas. A new method to estimate time delays between EEG signals applied on beta activity of the olfactory cortical areas. In this way we can asses, unambiguously, how the transmission of information between different brain sites develops.Д«oeijinga, P.H. It is demonstrated that from phase shifts we may estimate transmission delays between at least certain classes of EEG signals. This procedure is illustrated by applications to simulated and real EEG signals. From the slope of this phase function, a transmission delay can be estimated. By subtracting the minimum phase from the phase spectrum, a corrected phase function can be obtained. This involves computing the phase spectrum between the pair of signals, estimating the gain of the corresponding linear transfer function and the associated minimum phase. A procedure is described in order to solve this problem. However, a fundamental problem is that a phase shift may be difficult to interpret in terms of a biophysical model.

A basic question is whether such a phase shift can be interpreted as a transmission delay. The nature of the interdependence between EEG signals is not characterized only by the degree of association, but also by the corresponding phase relationship. The three methods were applied to simulated and real signals in order to put in evidence how nonlinear relationships may affect differently these three measures of association.

Three of these are described and their performance is compared: the cross-correlation (coherence and phase), the average amount of mutual information (AAMI) or the normalized AAMI, also called transmission coefficient T, and the correlation ratio h 2 that is a general measure of nonlinear fit between any two signals. To investigate the degree of interdependence of EEG signals, we have to use signal analysis methods.

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