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Advanced Biosignal Processing Chapter 7 From EEG Signals to Brain Connectivity - Methods and Applications in Epilepsy
Springer-Verlag Berlin Heidelberg, (2009)
Аннотация
During the past decades, considerable effort has been devoted to the
development of signal processing techniques aimed at quantifying
the temporal evolution of the cross-correlation (in a wide sense)
between signals recorded from spatially-distributed regions in order
to characterize brain functional connectivity during normal or pathological
(as in epilepsy) conditions. Besides linear methods introduced in
the field of EEG analysis fifty years ago, a number of studies have
been dedicated to the development of nonlinear methods, mostly because
of the
nonlinear nature of mechanisms at the origin of EEG signals. Recent
studies showed the potential value of methods commonly used in nonlinear
physics (see Chap. 15). Three families of methods (linear and nonlinear
regression, phase synchronization, and generalized synchronization)
are reviewed. Their performances are evaluated on the basis of a
simulation model in which a coupling parameter can be tuned between
populations of neurons generating bivariate EEG time-series. This
evaluation is performed according to quantitative criteria. The main
findings of this evaluation are the following. First, some of the
methods are insensitive to the coupling parameter. Second, results
were found to be dependent on signal properties. In particular, the
broadening of the frequency band is a parameter that strongly influences
the performances. Third, and generally speaking, there is no ‘universal’
method for measuring
statistical couplings among signals. Indeed, none of the studied methods
performs better than the other ones for the two studied situations
(background and epileptic activity). Finally, linear and nonlinear
regression methods were found to be sensitive to the coupling parameter
in all situations and showed either average or good performances.
This latter point leads the authors to conclude that these “robust”
methods should be applied before using more sophisticated methods
