• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

A random dipole model for spontaneous brain activity.

作者信息

de Munck J C, Vijn P C, Lopes da Silva F H

机构信息

Low Temperature Department, Technical University of Enschede, The Netherlands.

出版信息

IEEE Trans Biomed Eng. 1992 Aug;39(8):791-804. doi: 10.1109/10.148387.

DOI:10.1109/10.148387
PMID:1505993
Abstract

The statistical properties of the EEG and the MEG are described mathematically as the result of randomly distributed dipoles. These dipoles represent the interactions of cortical neurons. For certain dipole distributions, the first- and second-order moments of the electric and magnetic fields are derived analytically. If the dipoles are in a spherical volume conductor and have no preference for any direction, the variance of a differentially measured EEG-signal is only a function of the electrode distance. In this paper, the theoretically derived variance function will be compared with EEG- and MEG-measurements. It is shown that a dipole with a fixed position and a randomly fluctuating amplitude is an adequate model for the alpha-rhythm. An expression for the covariance between the magnetic field and a differentially measured EEG-signal is derived. This covariance is considered as a function of the magnetometer position, and is compared with the measurements of Chapman et al. [23]. The theory can be used to obtain a (spatial) covariance matrix of the background noise, which occurs in evoked potential measurements. Such a covariance matrix can be used to obtain a maximum likelihood estimator of the dipole parameters in evoked potential studies, to evaluate the merits of the so-called "Laplacian derivation," and for the interpolation of electromagnetic data.

摘要

相似文献

1
A random dipole model for spontaneous brain activity.
IEEE Trans Biomed Eng. 1992 Aug;39(8):791-804. doi: 10.1109/10.148387.
2
Direct reconstruction algorithm of current dipoles for vector magnetoencephalography and electroencephalography.用于矢量脑磁图和脑电图的电流偶极子直接重建算法。
Phys Med Biol. 2007 Jul 7;52(13):3859-79. doi: 10.1088/0031-9155/52/13/014. Epub 2007 Jun 4.
3
Confidence limits of dipole source reconstruction results.偶极子源重建结果的置信限。
Clin Neurophysiol. 2004 Jun;115(6):1442-51. doi: 10.1016/j.clinph.2004.01.019.
4
Effect of skull resistivity on the spatial resolutions of EEG and MEG.颅骨电阻率对脑电图(EEG)和脑磁图(MEG)空间分辨率的影响。
IEEE Trans Biomed Eng. 2004 Jul;51(7):1276-80. doi: 10.1109/TBME.2004.827255.
5
The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.时空脑磁图协方差矩阵建模为克罗内克积之和。
Neuroimage. 2005 Aug 15;27(2):402-15. doi: 10.1016/j.neuroimage.2005.04.015.
6
Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.用于脑磁图神经成像数据的时空贝叶斯推理偶极子分析
Neuroimage. 2005 Oct 15;28(1):84-98. doi: 10.1016/j.neuroimage.2005.06.003. Epub 2005 Jul 15.
7
Multiple dipole modeling and localization from spatio-temporal MEG data.基于时空脑磁图数据的多偶极子建模与定位
IEEE Trans Biomed Eng. 1992 Jun;39(6):541-57. doi: 10.1109/10.141192.
8
Statistical performance analysis of signal variance-based dipole models for MEG/EEG source localization and detection.用于脑磁图/脑电图源定位与检测的基于信号方差的偶极子模型的统计性能分析
IEEE Trans Biomed Eng. 2003 Feb;50(2):137-49. doi: 10.1109/TBME.2002.807661.
9
A resampling method for estimating the signal subspace of spatio-temporal EEG/MEG data.一种用于估计时空脑电/脑磁图数据信号子空间的重采样方法。
IEEE Trans Biomed Eng. 2003 Aug;50(8):935-49. doi: 10.1109/TBME.2003.814293.
10
Maximum-likelihood estimation of low-rank signals for multiepoch MEG/EEG analysis.用于多时段脑磁图/脑电图分析的低秩信号的最大似然估计
IEEE Trans Biomed Eng. 2004 Nov;51(11):1981-93. doi: 10.1109/TBME.2004.834285.

引用本文的文献

1
The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data.IAS-MEEG 包:用于从 M/EEG 数据中重建和可视化脑活动的灵活逆源重建平台。
Brain Topogr. 2023 Jan;36(1):10-22. doi: 10.1007/s10548-022-00926-9. Epub 2022 Dec 2.
2
CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition.CR-GCN:用于脑电图情感识别的基于通道关系的图卷积网络
Brain Sci. 2022 Jul 26;12(8):987. doi: 10.3390/brainsci12080987.
3
Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design.
基于广义空间频率分析和最优设计的脑磁图(MEG)和脑电图(EEG)的空间采样。
Neuroimage. 2021 Dec 15;245:118747. doi: 10.1016/j.neuroimage.2021.118747. Epub 2021 Nov 28.
4
Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation.头皮电磁传感器阵列的脑源定位优化设计。
Hum Brain Mapp. 2021 Oct 15;42(15):4869-4879. doi: 10.1002/hbm.25586. Epub 2021 Jul 10.
5
Measuring MEG closer to the brain: Performance of on-scalp sensor arrays.在更靠近大脑的位置测量脑磁图:头皮上传感器阵列的性能。
Neuroimage. 2017 Feb 15;147:542-553. doi: 10.1016/j.neuroimage.2016.12.048. Epub 2016 Dec 19.
6
Connectivity Measures in EEG Microstructural Sleep Elements.脑电图微观结构睡眠要素中的连通性测量
Front Neuroinform. 2016 Feb 17;10:5. doi: 10.3389/fninf.2016.00005. eCollection 2016.
7
Time-delayed mutual information of the phase as a measure of functional connectivity.相位延迟互信息作为功能连接的度量。
PLoS One. 2012;7(9):e44633. doi: 10.1371/journal.pone.0044633. Epub 2012 Sep 18.
8
Patterns of spontaneous magnetoencephalographic activity in patients with schizophrenia.精神分裂症患者自发脑磁图活动的模式。
J Clin Neurophysiol. 2010 Jun;27(3):179-90. doi: 10.1097/WNP.0b013e3181e0b20a.
9
On the EEG/MEG forward problem solution for distributed cortical sources.针对分布式皮质源的 EEG/MEG 正问题求解。
Med Biol Eng Comput. 2009 Oct;47(10):1083-91. doi: 10.1007/s11517-009-0529-x.
10
Cancellation of EEG and MEG signals generated by extended and distributed sources.取消由扩展和分布式源产生的 EEG 和 MEG 信号。
Hum Brain Mapp. 2010 Jan;31(1):140-9. doi: 10.1002/hbm.20851.