Although many sophisticated methods for analyzing electroencephalography (EEG) recordings have been developed, they are rarely used in clinical practice. To create robust EEG biomarkers that provide insight into the character of brain processes and distinguish mental disorders, we analyze neurodynamics using Recurrence Quantification Analysis (RQA) and simplify complex, non-stationary spatiotemporal oscillatory patterns using microstates. Transition patterns between microstates reflect brain dynamics. Average transition probability matrices between microstates may be used as reference prototypes for the classification of mental disorders. RQA enhances the feature space that microstate analysis provides, allowing for better interpretation of the results. We have tested this approach on adolescent schizophrenia data, comparing results based on microstate transitions with results based on features derived from RQA.