Quantitative analysis of EEG signals: Time-frequency methods and ...

3.5 Conclusion
In this chapter I described the use of quantitative parameters dened from the Gabor
Transform applied to the analysis of EEG signals. These parameters give an accurate
description of the time evolution of EEG rhythms, something dicult to perform from
the spectrograms. Spectrograms are more suitable for the analysis of long recordings
in which only large scale variations, of the order of minutes or hours, are relevant (see
examples of spectrogram analysis in Lopes da Silva, 1993b Gotman, 1990a). In this
context, spectrograms are for example very useful for discriminating between dierent
sleep stages, due to the slow variation of the frequency patterns. However, as showed in
sec. x3.3.2, spectrograms are not so suitable for the analysis epileptic seizures, in which
frequency patterns change in the order of seconds.
On the other hand, the quantication by means of the RIR allowed the performance
of a statistical analysis of the frequency content of Grand Mal seizures, by studying 20
scalp seizure recordings. This analysis showed marked decreases of the delta band during
the seizure due to the dominance of alpha and theta band at this stage. Since intracranial
recordings are nearly free of artifacts, the fact that the same pattern was seen in both
situations reinforces the idea that the results obtained with scalp electrodes were not
an spurious eect of muscle activity, orofthe procedure achieved for their elimination.
The introduction of the mean and maximum band frequencies allowed the study of
the time evolution of frequency patterns within each frequency band. It is interesting
to note that with this analysis it was possible to establish the presence of a well dened
delta oscillation during the seizure, in spite of the fact that its energy was very low and
remained latent until the ending of the seizure, when it reached very high amplitudes
and dominated the EEG.
Other interesting point to note is that although the grouping in frequency bands
implies a loose of frequency resolution, it can be more useful than the study of single
frequencies or peaks, due to the relations between frequency bands and functions or
sources of the brain.
The use of the present time-frequency analysis, together with the clinical patient
history and the visual assessment of the EEG, can contribute to the identication of the
source of epileptic seizures and to a better understanding of its dynamic.
Certainly, Gabor Transform is not intended to replace conventional EEG analysis,
but rather to complement it and also to provide further insights into the underlying
mechanisms of ictal patterns. In this context, RIR allows a fast interpretation of several
minutes of frequency variations in a single display, something dicult to perform with
traditional scalp EEG.
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