版权说明 操作指南
首页 > 成果 > 详情

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Cai, Chang*;Hashemi, Ali;Diwakar, Mithun;Haufe, Stefan;Sekihara, Kensuke;...
通讯作者:
Cai, Chang;Nagarajan, Srikantan S.
作者机构:
[Cai, Chang] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
[Diwakar, Mithun; Nagarajan, Srikantan S.; Cai, Chang; Cai, C] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.
[Hashemi, Ali; Haufe, Stefan] Charit Univ Med Berlin, Berlin Ctr Adv Neuroimagin, Berlin, Germany.
[Hashemi, Ali] Tech Univ Berlin, Elect Engn & Comp Sci Fac, Machine Learning Grp, Berlin, Germany.
[Hashemi, Ali] Tech Univ Berlin, Inst Math, Berlin, Germany.
通讯机构:
[Cai, Chang] C
[Cai, C; Nagarajan, SS] U
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
Univ Calif San Francisco, Dept Radiol & Biomed Imaging, San Francisco, CA 94143 USA.
语种:
英文
关键词:
Electromagnetic brain mapping;Robust noise estimation;Bayesian inference;Inverse problem;Magnetoencephalography
期刊:
NeuroImage
ISSN:
1053-8119
年:
2021
卷:
225
期:
3
页码:
117411
基金类别:
The authors would like to thank Danielle Mizuiri and Anne Findlay for collecting much of the MEG data, and all members and collaborators of the Biomagnetic Imaging Laboratory for their support. This work was supported by the National Natural Science Foundation of China under Grant 62007013, 61772380, and by NIH grants R01EB022717 , R01DC013979 , R01NS100440 , R01DC176960 , R01DC017091 , R01AG062196 UCOP-MRP-17-454755, and T32EB001631 from the NIBIB,and by the Major Project for Technological Innovation of Hubei Province under Grant 2019AAA044 and Science & Technology Major Project of Hubei Province (Next-Generation AI Technologies, no. 2019AEA170) , and by the European Research Council (ERC) under the European Union”s Horizon 2020 research and innovation programme (Grant agreement no. 758985 ). AH acknowledges scholarship support from the Machine Learning/Intelligent Data Analysis research group at Technische Universität Berlin and partial support from the Berlin International Graduate School in Model and Simulation based Research (BIMoS), the Berlin Mathematical School (BMS), and the Berlin Mathematics Research Center MATH+.
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com