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首页> 外文期刊>Communications in Nonlinear Science and Numerical Simulation >Different characteristics and important channels between the healthy brain network and the epileptic brain network based on EEG data
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Different characteristics and important channels between the healthy brain network and the epileptic brain network based on EEG data

机译：基于脑电数据的健康大脑网络与癫痫大脑网络之间的不同特征和重要通道

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In this paper, firstly, based on the clinic EEG data taken from 19 channels (electrodes, nodes) on human scalp, assuming that each electrode can be represented by a two-dimensional Rulkov chaotic neuron model, and the connectivity between any two electrodes may be the non-directional coupling, the healthy brain network and the epileptic brain network are established. Secondly, different dynamical characteristics between the two networks are investigated by the master stability function analysis (MSF). The research shows that the two networks are unlikely to achieve stable synchronization when the coupling is linear and alpha 2.8. When the coupling is nonlinear, there exist some a such that the epileptic brain network can achieve stable synchronization for epsilon is an element of [0.1678, 0.1694], while the healthy brain network is unlikely to achieve stable synchronization. Finally, based on graph theory and index of node importance, several kinds of evaluation indexes of node are calculated, such as degree, average path length and clustering coefficient. This investigation shows that the average degree and the average clustering coefficient of the epileptic brain network are larger than those of the healthy network. However, the average path length of the epileptic brain network is smaller than that of the healthy network. For other indexes, compared with the corresponding indexes of the healthy brain network, subgraph centricity, eigenvector, closeness and cumulated nomination of the epileptic brain network are increased or decreased, simultaneously. Channels Fpl, T5, Pz and Fp2 can be regarded as important focal zones for the occurrence of epilepsy. (C) 2018 Elsevier B.V. All rights reserved.
机译：在本文中，首先，基于从人头皮上19个通道（电极，节点）获取的临床EEG数据，假设每个电极都可以由二维Rulkov混沌神经元模型表示，并且任意两个电极之间的连通性都可以作为非定向耦合，建立了健康的大脑网络和癫痫的大脑网络。其次，通过主稳定性函数分析（MSF）研究了两个网络之间的不同动力学特性。研究表明，当耦合为线性且alpha> 2.8时，这两个网络不太可能实现稳定的同步。当耦合是非线性的时，存在一些使得癫痫的脑网络可以实现稳定同步的ε的元素[0.1678，0.1694]，而健康的脑网络则不太可能实现稳定的同步。最后，基于图论和节点重要性指标，计算出节​​点的评价指标，如度，平均路径长度和聚类系数。这项研究表明，癫痫脑网络的平均程度和平均聚类系数要大于健康网络的平均程度和平均聚类系数。但是，癫痫脑网络的平均路径长度小于健康网络的平均路径长度。对于其他指标，与健康大脑网络的相应指标相比，癫痫脑网络的子图中心性，特征向量，亲密性和累积提名同时增加或减少。通道Fp1，T5，Pz和Fp2可以被认为是癫痫发生的重要病灶区。 （C）2018 Elsevier B.V.保留所有权利。

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