Abstracts

RATIONALE: The EEG can be regarded as a mixture of electrical potentials that originate from distinct sources. Independent component analysis (ICA) is a recently developed method that allows to decompose multivariate signals into their statistically most independent components. This preprocessing step might enhance features that are not available with other methods, by suppressing irrelevant components. We investigate the ability of this technique to improve focus localization in patients with mesial temporal lobe epilepsy (MTLE).
METHODS: We applied ICA on intracranial artifact free EEG recordings of the seizure free interval of 18 patients with unilaterial MTLE. Two different ICA-algorithms were used to decompose the EEG. Subsequently, the mean phase coherence, a well established measure of phase synchronization, was calculated for all channel combinations of both the original EEG and the independent components. Focus lateralization was done by comparing the degree of synchronization for the two hemispheres after averaging over time.
RESULTS: Higher values of the mean phase coherence were found predominantely for the focal hemisphere for both the original EEG and independent components.For a high percentage of patients it was possible to get a correct lateralization of the focal hemisphere. Although in general lower values of the mean phase coherence were found for the independent components, still a higher discriminartive power for the focal hemisphere was obtained from this method.
CONCLUSIONS: Lower values of the mean phase coherence found for the independent components indicate that ICA allows to decrease linear correlations that are known to increase values of synchronization measures. Furthermore, our results indicate that ICA can be helpfull to further improve the capability of the mean phase coherence to lateralize the focal area.
[Supported by: Deutsche Physikalischegemeinschaft]