Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities

Frontiers in Physiology

Diana Toscano-Tejeida¹  Iris Steinmann¹  Mathias Bähr²  Alexander Schlemmer³  Ulrich Parlitz³  Stefan Luther³  Melanie Wilke⁴  Inga Kottlarz⁵  Sebastian Berg⁷ 

[1] Department of Cognitive Neurology, University Medical Center Göttingen, Göttingen, Germany;Department of Neurology, University Medical Center Göttingen, Göttingen, Germany;German Center for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany;German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany;Institute for the Dynamics of Complex Systems, Georg-August-Universität Göttingen, Göttingen, Germany;Institute of Pharmacology and Toxicology, University Medical Center Göttingen, Göttingen, Germany;Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany;

关键词: EEG - Electroencephalogram;    t-SNE (t-distributed stochastic neighbor embedding);    ordinal pattern statistics;    nonlinear dimensionality reduction;    biomarkers;    functional connectivity;   

DOI  :  10.3389/fphys.2020.614565

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown