ArticleScientific reports2020
Collective almost synchronization-based model to extract and predict features of EEG signals.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 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
- Constructing representative group networks from tractography: lessons from a dynamical approach.Frontiers in network physiology · 2024Article
- Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.Neural computation · 2023Article
- A modification to the Kuramoto model to simulate epileptic seizures as synchronization.Journal of mathematical biology · 2023Article
- How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment.Journal of neural engineering · 2023Article
- Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals.Scientific reports · 2022Article
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4 authors.
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Abstract
Understanding the brain is important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models were adjusted to reproduce data collected from the brain. One of the most commonly used types of data in neuroscience comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this study, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh-Rose neurons or Kuramoto oscillators), set to operate in a dynamic regime recognized as Collective Almost Synchronization (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76 s in the future. The average forecasting error was 9.22%. The random Kuramoto model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.
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