Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities
2021 | journal article. A publication with affiliation to the University of Göttingen.
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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. & Wilke, M. et al. (2021)
Frontiers in Physiology, 11. DOI: https://doi.org/10.3389/fphys.2020.614565
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- Authors
- Kottlarz, Inga; Berg, Sebastian ; Toscano-Tejeida, Diana; Steinmann, Iris; Bähr, Mathias ; Luther, Stefan ; Wilke, Melanie ; Parlitz, Ulrich ; Schlemmer, Alexander
- 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.
- Issue Date
- 2021
- Publisher
- Frontiers Media S.A.
- Journal
- Frontiers in Physiology
- eISSN
- 1664-042X
- Language
- English