ArticleFrontiers in physiology2020
Extracting Robust Biomarkers From Multichannel EEG Time Series Using Nonlinear Dimensionality Reduction Applied to Ordinal Pattern Statistics and Spectral Quantities.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Stationarity assessment of resting state condition via permutation entropy on EEG recordings.Scientific reports · 2025Article
- Remote effects of a 7-week combined stretching and foam rolling training intervention of the plantar foot sole on the function and structure of the triceps surae.European journal of applied physiology · 2023Article
- Get a New Perspective on EEG: Convolutional Neural Network Encoders for Parametric t-SNE.Brain sciences · 2023Article
- Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?Network neuroscience (Cambridge, Mass.) · 2022Article
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
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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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