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Journal Article

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

MPS-Authors
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Kottlarz,  Inga
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Berg,  Sebastian
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173583

Luther,  Stefan
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

/persons/resource/persons173613

Parlitz,  Ulrich
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Schlemmer,  Alexander
Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Kottlarz, I., Berg, S., Toscano-Tejeida, D., Steinmann, I., Bähr, M., Luther, S., et al. (2021). Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities. Frontiers in Physiology, 11: 614565. doi:10.3389/fphys.2020.614565.


Cite as: https://hdl.handle.net/21.11116/0000-0007-E8BF-D
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.