ArticleFrontiers in computational neuroscience2015
Stochastic non-linear oscillator models of EEG: the Alzheimer's disease case.
Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.Frontiers in neuroinformatics · 2025Article
- Information Geometry Theoretic Measures for Characterizing Neural Information Processing from Simulated EEG Signals.Entropy (Basel, Switzerland) · 2024Article
- On the Influence of Aging on Classification Performance in the Visual EEG Oddball Paradigm Using Statistical and Temporal Features.Life (Basel, Switzerland) · 2023Article
- Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer's Disease Recognition.Frontiers in aging neuroscience · 2022Article
- Collective almost synchronization-based model to extract and predict features of EEG signals.Scientific reports · 2020Article
- Annealed Importance Sampling for Neural Mass Models.PLoS computational biology · 2016Article
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3 authors.
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Abstract
In this article, the Electroencephalography (EEG) signal of the human brain is modeled as the output of stochastic non-linear coupled oscillator networks. It is shown that EEG signals recorded under different brain states in healthy as well as Alzheimer's disease (AD) patients may be understood as distinct, statistically significant realizations of the model. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) resting conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing-van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects by selecting the model physical parameters and noise intensity. The selected signal characteristics are power spectral densities in major brain frequency bands Shannon and sample entropies. These measures allow matching of linear time varying frequency content as well as non-linear signal information content and complexity. The main finding of the work is that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. However, it is also shown that the inclusion of sample entropy in the optimization process, to match the complexity of the EEG signal, enhances the stochastic non-linear oscillator model performance.
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