Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Magnetoacoustic tomography with magnetic induction for high-resolution bioimepedance imaging through vector source reconstruction under the static field of MRI magnet.

Medical physics·2014
Same author

Hollow superparamagnetic PLGA/Fe3O4 composite microspheres for lysozyme adsorption.

Nanotechnology·2014
Same author

[A bird's eye view of the algorithms and software packages for reconstructing phylogenetic trees].

Dong wu xue yan jiu = Zoological research·2014
Same author

Functional and biodegradable dendritic macromolecules with controlled architectures as nontoxic and efficient nanoscale gene vectors.

Biotechnology advances·2014
Same author

[Effects of artificial vegetation on the spatial heterogeneity of soil moisture and salt in coastal saline land of Chongming Dongtan, Shanghai].

Ying yong sheng tai xue bao = The journal of applied ecology·2014
Same author

TRIM14 is a mitochondrial adaptor that facilitates retinoic acid-inducible gene-I-like receptor-mediated innate immune response.

Proceedings of the National Academy of Sciences of the United States of America·2014

相关实验视频

Updated: May 9, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.4K

基于深度学习的EEG源成像在不同的电极配置下是强大的.

Jesse Rong1, Rui Sun1, Boney Joseph2

  • 1Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
|May 3, 2025
PubMed
概括
此摘要是机器生成的。

基于深度学习的源成像 (DeepSIF) 使用低密度EEG准确地定位大脑活动,克服了传统方法的局限性. 这种深度学习方法在不需要高密度电脑电图 (EEG) 设备的情况下对临床应用具有前景.

关键词:
深度神经网络 深度神经网络电极号码,电脑电图 (EEG) 电极号码电生理学源 影像成像 影像成像源地定位 源地定位

更多相关视频

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.2K

相关实验视频

Last Updated: May 9, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.4K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K
Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings
08:20

Brain Source Imaging in Preclinical Rat Models of Focal Epilepsy using High-Resolution EEG Recordings

Published on: June 6, 2015

15.2K

科学领域:

  • 神经科学是一个神经科学.
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 传统的脑电图源成像 (ESI) 需要高密度的EEG以获得准确性,这限制了临床使用.
  • 深度学习方法直接从数据中学习时空大脑活动,提供潜在的改进.
  • 低密度EEG在临床环境中更容易获得,但传统上产生不太可靠的ESI.

研究的目的:

  • 评估基于深度学习的新型源图像框架 (DeepSIF) 的性能.
  • 评估不同的EEG电极数量对DeepSIF准确性的影响.
  • 将DeepSIF与使用模拟和临床数据的传统ESI方法进行比较.

主要方法:

  • 进行了计算机模拟和对27名患者的临床数据的分析.
  • 使用从16到75个电极的通道配置评估了EEG源成像性能.
  • DeepSIF与sLORETA和LCMV传统方法进行了比较,并与地面真相和临床参考进行了比较.

主要成果:

  • 在所有测试的通道计数和噪声水平上,DeepSIF在源定位和范围估计方面表现出一致的准确性.
  • 在准确性方面,DeepSIF显著超过了传统方法 (sLORETA,LCMV).
  • 在患者中,DeepSIF实现了7.9/9.0毫米 (75/16电极) 的平均空间分散,相比之下,sLORETA的21.9/28.1毫米和LCMV的20.0/28.9毫米.

结论:

  • 对于EEG源成像,DeepSIF算法表现出强大的性能,即使使用低密度EEG.
  • 使用较少电极的DeepSIF的有效性表明其广泛的临床适用性.
  • 这种深度学习方法消除了在临床源成像中使用高密度EEG设备的需求.