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Front. Hum. Neurosci. | doi: 10.3389/fnhum.2019.00098

Adjustment of Synchronization Stability of Dynamic Brain-Networks based on Feature Fusion

  • 1Taiyuan University of Technology, China

One aspect of difficult-to-cure neurological diseases is a lack of or an anomaly of the brain's overall or local integration processing. When these work together, the process is referred to as the synchronization phenomenon in neurobiological theory. By studying the synchronization capabilities of the brain-network, we can intensively describe and characterize both the state of the interactions between brain regions and their differences between people with a mental illness and a set of controls by measuring the rapid changes in brain activity in patients with psychiatric disorders and the strength and integrity of their entire brain network. This is significant for the study of mental illness. In view of the shortcomings of the static connection method, this paper introduces the concepts of "dynamic" and "time-varying", constructs an EEG brain function network based on dynamic connection, and analyzes the time-varying characteristics of the EEG functional network. We used the spectral features of the brain network to extract its synchronization features and used the synchronization features to describe the evolutionary process and differences in the brain network’s synchronization ability between a group of patients and a control group during a working memory task. We propose a method based on the fusion of traditional features and spectral features to achieve an adjustment of the patient's brain network synchronization ability, so that its synchronization ability can be made to be consistent with that of healthy subjects, theoretically achieving the purpose of the treatment of diseases. Studying the stability of brain network synchronization can provide new ideas about the pathogenic mechanism and cure of mental diseases and has a wide range of application possibilities.

Keywords: EEG, working memory, EEG dynamic brain network, brain network synchronization stability, brain network Synchronization adjustment and control

Received: 28 Jul 2018; Accepted: 04 Mar 2019.

Edited by:

Mikhail Lebedev, Duke University, United States

Reviewed by:

Arthur Bikbaev, Leibniz Institute for Neurobiology (LG), Germany
Axel Hutt, German Weather Service, Germany  

Copyright: © 2019 Yao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Haifang Li, Taiyuan University of Technology, Taiyuan, China,