Collective almost synchronization-based model to extract and predict features of EEG signals
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This study proposes 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).Abstract:
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%.read more
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References
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Random graphs
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
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Hervé Abdi,Lynne J. Williams +1 more
TL;DR: Principal component analysis (PCA) as discussed by the authors is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables, and its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and display the pattern of similarity of the observations and of the variables as points in maps.
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