脑电图
信号(编程语言)
大脑活动与冥想
叠加原理
各向同性
头皮
磁共振弥散成像
神经科学
人脑
计算机科学
各向异性
物理
部分各向异性
模式识别(心理学)
人工智能
生物系统
磁共振成像
心理学
光学
医学
生物
解剖
放射科
量子力学
程序设计语言
作者
Svea Petersen,Ulf Zimmermann,Christian Schmidt,Lars Schwabe,Mareike Warkentin,Stefan J. Teipel
出处
期刊:Biomedizinische Technik
[De Gruyter]
日期:2014-01-01
卷期号:59 (3)
标识
DOI:10.1515/bmt-2013-0062
摘要
Electroencephalography (EEG) is often employed to measure electrical activity in the living human brain. Simulation studies can help unravel how the brain electrical activity pattern generates the EEG signal, still a widely unresolved question. This article describes a method to simulate brain electrical activity by using neuronal populations of a neural mass model. Implementing these populations in a finite element model of the head offers the opportunity to investigate the influence of each group of neurons to the scalp potential. This model is based on structural magnetic resonance imaging data to specify tissue composition, and diffusion tensor imaging data to model local anisotropy. We simulated the EEG signals of five neuronal populations generating α waves in the visual cortex. Our results indicate that radially oriented sources dominate over tangential sources in the generation of the scalp signal. Investigating the influence of anisotropic conductivity, we found small differences in topography and phase and larger ones for the potential amplitude compared with an isotropic conductivity distribution. The outcome of this article is a fast method based on superposition of sources for simulating time-dependent EEG signals, which can be used for further studies of neurodegenerative diseases.
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