Collective Almost Synchronization-based model to extract and predict features of EEG signals

Phuong Thi Mai Nguyen, Yoshikatsu Hayashi, Murilo Da Silva Baptista, Toshiyuki Kondo* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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%.

Original languageEnglish
Article number16342
Pages (from-to)16342
Number of pages16
JournalScientific Reports
Volume10
Early online date1 Oct 2020
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Biophysical models
  • Computational models
  • Computational neuroscience
  • HURST EXPONENT
  • NETWORKS
  • BRAIN DYNAMICS

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