.png){kind=icon}
返回
Probabilistic forward model for electroencephalography source analysis
.png){kind=icon}
OA
AI
DOI：10.1088/0031-9155/52/17/014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.
Keyword:
ELECTRICAL-IMPEDANCE TOMOGRAPHY
DIPOLE LOCALIZATION ACCURACY
HUMAN SKULL
HUMAN HEAD
EEG
CONDUCTIVITY
MEG
SIMULATION
COMPUTATION
ANISOTROPY
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
机构
暂无机构信息
引用论文
Microfluidic Based Fabrication and Characterization of Highly Porous Polymeric Microspheres.png)
PolymersOAAI
PolymersIF0
OAAI分享收藏
查看更多内容