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

able for automatic detection processes or on-line analysis (Jansen, 1991). Furthermore,
other methods turned out to be more suitable for these purposes (see Pjin et al., 1997
Gotman, 1990b).
Since it was reported that ltered noise could lead to converging low D 2 values,
conclusions based on absolute values of D 2 should be revised and validated. In this
respect, the use of surrogate tests (Theiler et. al., 1992) was proposed. Furthermore,
absolute results should be stable with respect to dierent election of the parameters
necessary for calculations. This type of approach was mainly used for discriminating
between deterministic chaos vs. random behavior of the EEGs. In this respect, as I will
discuss in detail in section x7, the failure to validate converging low dimensional values
of D 2 with surrogate tests (then proving a low dimensional chaotic nature) implies that
methods of Chaos are not suitable for answering this question rather than implying a
random nature of the EEG signals.
I would like to remark that I discussed the most used approach of Chaos analysis
to EEG signals, namely, by means of the Correlation Dimension, Lyapunov exponents
or Kolmogorov entropy. Although their results are not so promising as believed before,
Chaos analysis is not limited to these invariants and alternative non linear approaches as
for example the study of chaotic synchronization (Arnhold et al., 1999 Quian Quiroga
et al., 1999c) could lead to interesting results.
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