In this study, ordinal pattern analysis and classical frequency-based EEG analysistextlessbrtextgreatermethods are used to differentiate between EEGs of different age groups as well astextlessbrtextgreaterindividuals. As characteristic features, functional connectivity as well as single-channeltextlessbrtextgreatermeasures in both the time and frequency domain are considered. We comparetextlessbrtextgreaterthe separation power of each feature set after nonlinear dimensionality reductiontextlessbrtextgreaterusing t-distributed stochastic neighbor embedding and demonstrate that ordinaltextlessbrtextgreaterpattern-based measures yield results comparable to frequency-based measures appliedtextlessbrtextgreaterto preprocessed data, and outperform them if applied to raw data. Our analysis yieldstextlessbrtextgreaterno significant differences in performance between single-channel features and functionaltextlessbrtextgreaterconnectivity features regarding the question of age group separation.