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

Proteomics01:33

Proteomics

7.2K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.2K
Mass Analyzers: Overview01:13

Mass Analyzers: Overview

615
The mass analyzer is a crucial component of the mass spectrometer. In the ionization chamber, the vaporized sample is bombarded with a high-energy electron beam to generate a radical cation and further fragment into neutral molecules, radicals, and cations. A series of negatively charged accelerator plates accelerate the cations into the mass analyzer. The mass analyzer separates ions according to their mass-to-charge (m/z) ratios and then directs them to the detector. The common types of mass...
615

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

Learned statistical regularity modulates anticipatory micro-saccades toward suppressed distractor locations.

Nature communications·2026
Same author

An open multi-center MEG-EEG dataset for studying conscious visual perception.

Scientific data·2026
Same author

Cross-modal interaction of human alpha activity does not reflect inhibition of early sensory processing in a frequency-tagging study using EEG and MEG.

eLife·2026
Same author

Hierarchical brain dynamics supporting visual perceptual transitions.

Science advances·2026
Same author

Canonical Hidden Markov Model Networks for studying M/EEG.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Effects of Age on Resting-State Cortical Networks.

Human brain mapping·2026
Same journal

Detection of cochlear microphonic for differential diagnosis between auditory neuropathy mice and noise-induced sensorineural hearing loss mice.

Journal of neuroscience methods·2026
Same journal

Assessment metrics for pain control in rats: A methodological commentary.

Journal of neuroscience methods·2026
Same journal

Infant EEG preprocessing pipelines: A capability framework and current gaps in practice.

Journal of neuroscience methods·2026
Same journal

Methods for measuring neural activity during voluntary wheel running.

Journal of neuroscience methods·2026
Same journal

Serotype-dependent differences in AAV cellular transduction rates in the hypothalamus of Arctic ground squirrels.

Journal of neuroscience methods·2026
Same journal

Rapid generation of human sensory neurons from iPSC for modeling of peripheral neuropathies.

Journal of neuroscience methods·2026
查看所有相关文章

相关实验视频

Updated: Jun 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

优化磁计阵列和分析管道,用于多变量模式分析.

Yulia Bezsudnova1, Andrew J Quinn1, Ole Jensen1

  • 1Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.

Journal of neuroscience methods
|September 12, 2024
PubMed
概括
此摘要是机器生成的。

对于磁脑学 (MEG) 中的多变量模式分析 (MVPA),大约30个传感器是足够的. 没有规则化的信号空间分离 (SSS) 会增加噪音并降低精度;相反,使用替代的降噪方法.

关键词:
梯度计 梯度计 梯度计 梯度计MVPA MVPA是什么意思磁性脑电图 (MEG) 是一种磁性脑电图.磁力计 磁力计 磁力计有光学的磁力计.优化优化 优化优化信号空间分离过器的信号空间分离过器.

更多相关视频

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

科学领域:

  • 神经科学是一个神经科学.
  • 生物物理学的生物物理.
  • 信号处理 信号处理

背景情况:

  • 多变量模式分析 (MVPA) 是认知神经科学中的一个强大的工具.
  • 基于光学磁仪的磁脑电图 (OPM-MEG) 显示出对MVPA应用的前景.

研究的目的:

  • 为 MVPA 实验优化 OPM-MEG 系统.
  • 确定磁力计的最佳降噪技术.
  • 在图像分类任务中确定强大的MVPA传感器的最小数量.

主要方法:

  • 从传统的MEG磁力计阵列中检查的数据.
  • 评估了带有和没有规范化的信号空间分离 (SSS) 的影响.
  • 将SSS与信号空间投影 (SSP) 和均质场校正 (HFC) 进行比较.

主要成果:

  • 没有适当规范化的SSS显著降低了102个磁力计或204个梯度计的分类准确性.
  • 不管SSS的应用如何,分类准确度没有提高超过30个传感器.
  • 与SSP或HFC相比,SSS过增加了噪音底层,导致MVPA解码结果较低.

结论:

  • 对于在图像分类中优化为MVPA的MEG系统,大约30个磁力计就足够了.
  • 建议不要在没有适当规范化MEG/OPM-MVPA的情况下使用SSS,因为宽带噪声增加.
  • 建议使用SSP,HFC和梯度降噪等降噪技术来维持或降低噪声底线.