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We introduce a novel approach in EEG data sonification for process monitoring and exploratory as well as comparative data analysis. The approach uses an excitory/articulatory speech model and a particularly selected parameter mapping to obtain auditory gestalts (or auditory objects) that correspond to features in the multivariate signals. The sonification is adaptable to patient-specific data patterns, so that only characteristic deviations from background behavior (pathologic features) are involved in the sonification rendering. Thus the approach combines data mining techniques and case-dependent sonification design to give an application-specific solution with high potential for clinical use. We explain the sonification technique in detail and present sound examples from clinical data sets.