Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan.
Biomedical Science/Engineering, School of Biological Sciences, University of Reading, Reading, RG6 6UR, UK.
Sci Rep. 2020 Oct 1;10(1):16342. doi: 10.1038/s41598-020-73346-z.
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%.
理解大脑在科学、医学和工程领域都很重要。一种有前途的方法是通过计算模型来更好地理解大脑。这些模型被调整以复制从大脑中收集的数据。神经科学中最常用的数据类型之一来自脑电图（EEG），它记录了当大脑中的神经元被激活时产生的微小电压。在这项研究中，我们提出了一个基于弱连接动力系统的复杂网络模型（Hindmarsh-Rose 神经元或 Kuramoto 振荡器），设定为在集体几乎同步（CAS）的动态模式下运行。我们的模型不仅成功地复制了来自健康和癫痫 EEG 信号的 EEG 数据，而且还预测了 EEG 特征、赫斯特指数和功率谱。所提出的模型能够预测未来 5.76 秒的 EEG 信号。平均预测误差为 9.22%。随机 Kuramoto 模型在预测癫痫 EEG 方面取得了出色的成绩，误差为 11.21%。
