In this study, ordinal pattern analysis and classical frequency-based EEG analysis methods are used to differentiate between EEGs of different age groups as well as individuals. As characteristic features, functional connectivity as well as single-channel measures in both the time and frequency domain are considered. We compare the separation power of each feature set after nonlinear dimensionality reduction using t-distributed stochastic neighbor embedding and demonstrate that ordinal pattern-based measures yield results comparable to frequency-based measures applied to preprocessed data, and outperform them if applied to raw data. Our analysis yields no significant differences in performance between single-channel features and functional connectivity features regarding the question of age group separation.
Keywords: EEG - Electroencephalogram; biomarkers; coherence; functional connectivity; mutual information; nonlinear dimensionality reduction; ordinal pattern statistics; t-SNE (t-distributed stochastic neighbor embedding).
Copyright © 2021 Kottlarz, Berg, Toscano-Tejeida, Steinmann, Bähr, Luther, Wilke, Parlitz and Schlemmer.