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

agation time. They applied this procedure to study ERPs in central and parietal
locations, nding lower values in non-target than in target stimulus, without signicant
dierences between electrodes. In the central location (Cz) they report a value of
=0:397 0:18 for non-target stimulus and of =0:794 0:44 for the target ones.
Although there is a great variance in the results of dierent groups, there is a general
agreement that EEG signals have at least one positive Lyapunov exponent, implying
that EEGs (in case of having a deterministic origin) reect a chaotic activity.
5.5 Application to scalp recorded EEGs
5.5.1 Material and Methods
EEG recordings of 6 subjects were studied. Recordings were performed in a \no-task"
awake state with eyes closed. Three of these subjects had normal EEG recordings ( N1,
N2, N3 ) and other three had abnormal ones (A1,A2,A3). The main abnormalities
of these last ones were: slow waves, increases in theta rhythms, very low alpha reactivity
and important decrease in the alpha/theta ratio.
EEGs were digitized using a 8 bits analog-to-digital (A/D) converter. 20 electrodes
were disposed according to the 10 ; 20 system with earlobe references. The data was
sampled with a frequency of 256 Hz, and ltered with a high-pass lter at 0.5 Hz and
alow-pass lter at 32 Hz. We chose for our analysis the central electrodes because they
were the ones less contaminated by artifacts.
5.5.2 Results and Discussion
The objective of this section is to establish some criteria for the analysis of EEG signals
previous to any calculation with Chaos methods. This is done in order to avoid spurious
results due to unfortunate selections of the segments of data to be analyzed.
The rst step is to choose data segments without artifacts. Then, after selecting
proper zones free of artifacts or with very few interruptions, the stationarity of the data
can be veried by applying the criteria described in sec. x5.3. In this case, the length of
the data bins was 1000 data points (about 4 seconds of digitized EEG signal).
In Fig. 26 and Fig. 27 the mean and variance for the time series denoted by N1
are showed. In this case, mean and variance were considered stable when their changes
were in a range of 20% (see sec. 5.3). From these gures it can be concluded that the
stationarity criterion previously proposed is satised between bins 9 and 26. Note that
in this case, the variance does not give any restriction about selected bins to be used in
the following analysis.
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