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

P300 deection.
Due to the relation between the P300 and the decreases in the entropy, it can be
tentatively assumed that the cognitive (P300) response involves a higher degree of order
than the one related with the ongoing EEG and the one related with the P100 response.
From this, we can postulate that the cognitive response is possibly related with a tuning
of the EEG oscillations, thus giving a narrower frequency response reected in a low
entropy value. However, this conjecture should be veried with more subjects and with
dierent experiments. Furthermore, since in this case the WS was dened from the
mean wavelet coecients (after averaging the coecients of the single trials) we can
not discard the possibility of other oscillations being present in a non phase-locked way
(therefore their contribution being cancelled when averaging).
The importance of delta oscillations in the generation of the P300 was reported
in several previous works. Basar-Eroglu et al. (1992) by using an auditory oddball
paradigm in 10 subjects, pointed out the importance of delta oscillators in the generation
of the P300, suggesting that they are related mainly with decision making and matching.
Schurmann et al. (1995), also remarked the importance of delta oscillations in the
generation of the P300, nding also delta enhancement uponTARGET responses in the
single trials. Furthermore, they obtain better averaged responses by selectively averaging
single trials with an enhanced delta response. A similar result was recently reported by
Demiralp et al. (1999), who after applying a wavelet multirresolution decomposition to
evoked responses, used the delta coecients as discriminators between good and bad
single trials.
6.4 Conclusions
Wavelet-entropy proved to be a very useful tool for characterizing the event-related
responses. With this method a new approach to the measure of order/noise of a system
can be achieved 8 . Up to now, this type of descriptions were merely based on Chaos
analysis. However, these methods have several prerequisites, some of them making their
application to the study of EEG signals impossible. One of these prerequisites is the
data length. Chaos analysis cannot be applied to short data recordings as in the case of
event-related potentials. In this type of signals, the dynamics of the system is changing
completely in fractions of a second duetotheeectof the stimulus, and then it has no
sense to dene an attractor and to calculate parameters as the Correlation Dimension,
8
We should remark that high WS values does not necesarily mean noise since for example chaotic
deterministic systems also have a broadband spectra. However, analysis of how the EEG gets tuned in
frequency after stimulation is directly related with the question of the deterministic/random nature of
it as it will be further discussed in sec.7.1.3.
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