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

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.

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 Creators:
Kottlarz, Inga1, Author              
Berg, Sebastian1, Author              
Toscano-Tejeida, Diana, Author
Steinmann, Iris, Author
Bähr, Mathias, Author
Luther, Stefan1, Author              
Wilke, Melanie, Author
Parlitz, Ulrich1, Author              
Schlemmer, Alexander1, Author              
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1Research Group Biomedical Physics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063288              

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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.

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Language(s): eng - English
 Dates: 2021-02-012021
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.3389/fphys.2020.614565
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Title: Frontiers in Physiology
Source Genre: Journal
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Pages: 11 Volume / Issue: 11 Sequence Number: 614565 Start / End Page: - Identifier: ISSN: 1664-042X