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Abstract:
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.
