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首页> 外文会议>6th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems >Classification of EEG in eyes-open and eyes-closed state based on limited penetrable visibility graph
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Classification of EEG in eyes-open and eyes-closed state based on limited penetrable visibility graph

机译：基于有限的可穿透性可见度图的睁眼和闭眼状态下的脑电图分类

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摘要

Eyes-open and eyes-closed are two physiological states with different levels of brain activity. To classify EEG in eyes-open and eyes-closed, limited penetrable visibility graph method is applied to analyze EEG. It is a signal process method based on graph theory, which bridges complex networks and EEG time series. Networks topology, average path length and clustering coefficient are used to depict EEG characteristics respectively. The results show that limited penetrable visibility graph is more effective mehod than visibility graph for classifying EEG in eyes-open and eyes-closed. Clustering coefficient obtained by limited penetrable visibility graph is statistically significantly higher in eyes-closed condition and is a valid marker of eyes-closed EEG. There is no obvious difference in average path length. Limited penetrable visibility graph provides a new idea and an effective approach in classifying EEG time series.
机译：睁眼和闭眼是两种大脑活动水平不同的生理状态。为了对睁开和闭眼的脑电图进行分类，采用有限穿透性可见度图法对脑电图进行分析。它是一种基于图论的信号处理方法，将复杂的网络和EEG时间序列联系起来。网络拓扑，平均路径长度和聚类系数分别用于描述脑电图特征。结果表明，有限的穿透可见度图比可见度图更有效地分类睁眼和闭眼时的脑电图。在闭眼条件下，有限的可穿透可见性图获得的聚类系数在统计学上显着更高，并且是闭眼EEG的有效标志。平均路径长度没有明显差异。有限的可渗透能见度图为脑电时间序列分类提供了新思路和有效方法。

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