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

information that two dierent stimuli are manifestations of the same process. However,
the possibility thatanintensity rise on joined-polymodal conditions leads to behavioral
changes in terms of attention and arousal functions can not be discarded and deserves
further studies.
Less data is available on the role of 40 Hz-oscillatory processes in bisensory integration.
Sheer and Schrock (1986) conducted their studies on focused attention by looking
for modications in 40 Hz-SSRs (steady-state responses, seesec.x1.2) driven by means of
simultaneously applied click-and-ash stimuli, from which one or the other modality had
to be ignored. With the stimulus trains being not in phase, they found the 40 Hz-SSRs
of the non-focused stimulus modality reduced. Other modes of intermodal perturbation
of the 40 Hz-SSRs have been tested by Rohrbaugh et al. (1990). Both salient \foreground"
visual and auditory stimuli implied a latency and amplitude decrease in the
40Hz auditory steady-state responses, stimuli of the same modality thereby dominating
in eect. Thus both exemplary studies have discriminative functions in common, which
are modiers of the 40Hz surface EEG.
4.7 Conclusion
Wavelet decomposition proved to be a very useful tool for analyzing brain signals. I
would like to remark two advantages of Wavelet Transform over Fourier based methods.
First, Wavelet Transform lacks of the requirement of stationarity, this being crucial for
avoiding spurious results when analyzing brain signals, already known to be highly nonstationary.
Second, owing to the varying window sizeoftheWavelet Transform, a better
time-frequency resolution can be achieved when the signal has patterns involving dierent
scales. This is particularly important inthe case of event-related potentials, where
the relevant response is limited to a fraction of a second, a good time resolution thus
being crucial for making any physiological interpretation. In section 4.5.2, I showed
with some selected sweeps how the multiresolution decomposition implemented with
B-Splines functions leads to a better resolution of the event-related oscillations in comparison
with a conventional ideal lter used in several previous works. Moreover, due
to the fact that the multiresolution decomposition method is implemented as a ltering
scheme, it can be seen as a way to construct lters with an optimal time-frequency resolution.
This exemplies and complements the theoretical description of the advantages
of wavelets introduced in section 4.2. Furthermore, the multiresolution decomposition
is a way of data reduction, thus giving relevant (i.e. non-redundant) coecients that
allows a straightforward implementation of statistical tests.
Alpha responses to pattern visual event-related potentials were related with primary
sensory processing, having several generators. Furthermore, the analysis of discrete
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