Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

fNIRS single-trial decoding improves systematically with higher optode density, model-based noise regression, and image reconstruction.

Journal of neural engineering·2026

Same author

Surface-based image reconstruction optimization for high-density functional near-infrared spectroscopy.

Neurophotonics·2026

Same author

Phantom for standardization in functional near-infrared spectroscopy, part 1: implementation and usage.

Neurophotonics·2025

Same author

Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.

Imaging neuroscience (Cambridge, Mass.)·2025

Same author

Quantifying the impact of hair and skin characteristics on fNIRS signal quality for enhanced inclusivity.

Nature human behaviour·2025

Same author

Publisher Correction: fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience.

Communications biology·2025

Search research articles

相关实验视频

Updated: Jan 13, 2026

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

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

Published on: September 20, 2024

从数字化电极位置或光谱学数据驱动的头部模型个性化改进了M/EEG源定位精度.

Nils Harmening^1,2,3, Alexander von Lühmann^1,2, Benjamin Blankertz³

¹BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.

Imaging neuroscience (Cambridge, Mass.)

|January 12, 2026

概括

这项研究引入了一种新的算法,可以在没有MRI/CT扫描的情况下创建个性化的头部模型,从而提高脑源定位精度,用于脑电图 (EEG) 和脑磁图 (MEG) 研究.

关键词:

在 BEMEM 中,你会发现.这是一个EEGEEGEEGEEGEEGEEGEEG.在MEG MEG中,我们可以使用MEG.头部建模模的头部建模反向问题反向问题摄影计量 (摄影计量) 是一种摄影仪.源代码本地化源代码本地化

更多相关视频

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Published on: September 9, 2025

相关实验视频

Last Updated: Jan 13, 2026

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

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

Published on: September 20, 2024

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Personalized 3D-printed Headgear for Multi-electrode Transcranial Electrical Stimulation

Published on: September 9, 2025

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
医疗成像医学成像

背景情况:

在电脑图 (EEG) 和磁脑图 (MEG) 中,精确的源定位依赖于精确的头部模型.
标准的头部模型 (例如,Colin27,ICBM-152) 缺乏个体解剖学准确性,可能会限制源定位性能.
在许多M/EEG研究中,获得单个结构性MRI/CT扫描通常是不切实际的或不可用的.

研究的目的:

开发和验证一个数据驱动的算法,以仅使用头皮信息来近似个体头部解剖.
通过使用个性化的头部模型,提高M/EEG中脑源定位的准确性.
在结构MRI/CT数据无法获取时,为改进源定位提供实用解决方案.

主要方法:

用一个头部模型数据库的低维表示来推导单个头部形状参数.
从摄影测量或电极位置 (甚至从智能手机扫描) 中获得的头皮信息被用来个性化头部模型.
为了验证,对16名受试者进行了一项实验研究和对22名头部进行了一项EEG模拟研究.

主要成果:

与现有方法相比,拟议的算法产生了更准确的头部模型解剖学,即使使用智能手机衍生的头皮数据.
与标准和其他个性化方法相比,个性化的头部模型在EEG模拟中显著提高了源定位的准确性.
该方法在近似个体头部形状和提高定位精度方面表现出卓越的性能.

结论:

拟议的数据驱动算法为创建个性化头部模型提供了一种可行的方法,而不需要结构性MRI/CT扫描.
这种方法可以大大提高M/EEG研究的源本地化准确性,并尽量减少额外的努力.
这些发现表明,在各种研究和临床应用中,M/EEG的实用性可以提高.