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

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same author

Effects of encapsulated algae oil supplements on the production of docosahexaenoic acid-enriched milk in mid-lactation dairy cows.

JDS communications·2026
Same author

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

iScience·2026
Same author

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same author

A pathological morphology parameter-based prognostic nomogram for high-risk prostate cancer patients treated with neoadjuvant therapy followed by radical prostatectomy: a retrospective study.

World journal of surgical oncology·2026
Same author

Advances in Interleukin-2 Engineering and Delivery Systems for Cancer Immunotherapy.

ACS applied bio materials·2026

相关实验视频

Updated: Jun 24, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K

[在单通道/少数通道EEG信号中处理生理工件的方法]

Guojing Wang1,2, Hongyun Liu2, Weidong Wang2

  • 1School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191.

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
|June 12, 2024
PubMed
概括
此摘要是机器生成的。

本综述涵盖了从单通道/少数通道电脑电图 (EEG) 记录中删除生理学文物的方法. 它分析了各种技术及其适用于不同场景的适用性,以提高EEG数据质量.

关键词:
工艺品 工艺品是一种艺术品.机器学习是机器学习.混合方法是一种混合方法.单通道/少数通道的EEG.

更多相关视频

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

32.7K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

10.7K

相关实验视频

Last Updated: Jun 24, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
10:35

Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

Published on: June 3, 2013

32.7K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

10.7K

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
  • 信号处理 信号处理

背景情况:

  • 脑电图 (EEG) 是一种重要的非侵入性技术,用于测量大脑的电活动.
  • 越来越多地使用单通道/少数通道EEG系统受到生理学工件的阻碍,这些工件会损害数据分析和应用.
  • 脑电图信号中的人工物可以来自各种生理来源,扭曲真正的神经信息.

研究的目的:

  • 综合审查现有的生理工件移除方法,以单通道/少数通道EEG.
  • 分析和总结针对特定场景 (例如,单个与多个文物,在线与离线处理) 量身定制的混合文物删除策略.
  • 讨论验证指标和趋势在单通道/几通道EEG人工物处理.

主要方法:

  • 审查回归和过技术,以删除文物.
  • 对源分离的分解方法 (例如,独立组件分析) 的分析.
  • 机器学习方法的评估应用于EEG工件检测和校正.

主要成果:

  • 确定了各种既定和新兴的方法来消除EEG中的生理学文物.
  • 基于信号特征和应用上下文的分类混合工件移除方法.
  • 对半模拟和真实EEG数据的性能验证指标进行了审查.

结论:

  • 有效的人工物清除对于单通道/少数通道EEG的可靠应用至关重要.
  • 混合方法为各种工件场景和处理要求提供有希望的解决方案.
  • 对人工物处理的进一步研究对于推进单通道/少数通道EEG应用至关重要.