Iakovidou Nantia D
Data Engineering Laboratory, Department of Informatics, Aristotle University of Thessaloniki , Thessaloniki, Greece .
Brain Connect. 2017 Apr;7(3):137-151. doi: 10.1089/brain.2016.0426. Epub 2017 Mar 29.
The brain is one of the largest and most complex organs in the human body and EEG is a noninvasive electrophysiological monitoring method that is used to record the electrical activity of the brain. Lately, the functional connectivity in human brain has been regarded and studied as a complex network using EEG signals. This means that the brain is studied as a connected system where nodes, or units, represent different specialized brain regions and links, or connections, represent communication pathways between the nodes. Graph theory and theory of complex networks provide a variety of measures, methods, and tools that can be useful to efficiently model, analyze, and study EEG networks. This article is addressed to computer scientists who wish to be acquainted and deal with the study of EEG data and also to neuroscientists who would like to become familiar with graph theoretic approaches and tools to analyze EEG data.
大脑是人体最大且最复杂的器官之一,脑电图(EEG)是一种用于记录大脑电活动的非侵入性电生理监测方法。最近,人类大脑中的功能连接已被视为并利用脑电图信号作为一个复杂网络来进行研究。这意味着大脑被当作一个连通系统来研究,其中节点或单元代表不同的特定脑区,而链接或连接代表节点之间的通信路径。图论和复杂网络理论提供了各种度量、方法和工具,可有效地对脑电图网络进行建模、分析和研究。本文面向希望了解并处理脑电图数据研究的计算机科学家,以及希望熟悉用于分析脑电图数据的图论方法和工具的神经科学家。