Abstract/Summary

Understanding the brain is an important in the fields of science, medicine, and engineering. A promising approach to better understand the brain is through computing models. These models arewere adjusted to reproduce data collected from the brain. One of the most commonlymostly used types of data in neuroscience is the electroencephalogram comes from electroencephalography (EEG), which records the tiny voltages generated when neurons in the brain are activated. In this workstudy, we propose a model based on complex networks of weakly connected dynamical systems (Hindmarsh- Rose neurons or Kuramoto oscillators), set to operate in a dynamicaldynamic regime recognized as the Collective Almost Synchronisation (CAS). Our model not only successfully reproduces EEG data from both healthy and epileptic EEG signals, but it also predicts the EEG features, the Hurst exponent, and the power spectrum. The proposed model is able to forecast EEG signals 5.76s 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%.