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- Title
Collective almost synchronization-based model to extract and predict features of EEG signals.
- Authors
Nguyen, Phuong Thi Mai; Hayashi, Yoshikatsu; Baptista, Murilo Da Silva; Kondo, Toshiyuki
- 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%.
- Subjects
ELECTROENCEPHALOGRAPHY; DIAGNOSIS of brain diseases; BRAIN physiology; NEURON analysis; BIOLOGICAL neural networks; FOURIER analysis; AUTOREGRESSIVE models
- Publication
Scientific Reports, 2020, Vol 10, Issue 1, pN.PAG
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-020-73346-z