Accessibility navigation


Collective almost synchronization-based model to extract and predict features of EEG signals

Nguyen, P. T. M., Hayashi, Y., Baptista, M. D. S. and Kondo, T. (2020) Collective almost synchronization-based model to extract and predict features of EEG signals. Scientific Reports, 10 (1). 16342. ISSN 2045-2322

[img]
Preview
Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

3MB
[img]
Preview
Text - Supplemental Material
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.

5MB
[img] Archive - Supplemental Material
· Restricted to Repository staff only
· The Copyright of this document has not been checked yet. This may affect its availability.

235kB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1038/s41598-020-73346-z

Abstract/Summary

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%.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Biological Sciences > Biomedical Sciences
ID Code:93179
Uncontrolled Keywords:Article, /631/114/116/2392, /631/114/2397, /631/114/116, article
Additional Information:** From Springer Nature via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ ** Journal IDs: eissn 2045-2322 ** Article IDs: publisher-id: s41598-020-73346-z; manuscript: 73346 ** History: collection 12-2020; online 01-10-2020; published_online 01-10-2020; registration 16-09-2020; accepted 15-09-2020; submitted 11-06-2020
Publisher:Nature Publishing Group UK

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation