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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

212
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
212

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

JA-CmMYC2 and SA-CmWRKY40 signaling networks relieve the inhibition of CmTLP15/22 on the cold tolerance of melon seedlings.

The Plant journal : for cell and molecular biology·2026
Same author

A dual track predictive model for assessing crow's feet aging across different clinical severity grades.

Scientific reports·2026
Same author

Respiratory Variability in Disorders of Consciousness: Relationship and Clinical Applications.

CNS neuroscience & therapeutics·2026
Same author

Identification of novel CRESS-DNA viruses in the human vaginal microbiome.

Frontiers in microbiology·2026
Same author

Illustrations of Handbook of the Birds of the World: Datasets of RGB values and color classification of birds.

Ecology·2026
Same author

Diagnostic value of somatosensory evoked potentials for paroxysmal sympathetic hyperactivity: a retrospective cohort study.

Frontiers in neurology·2026

相关实验视频

Updated: Jul 13, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K

一个三维的自适应性理性插值算法,用于删除TMS-EEG脉冲文物.

Hui Xiong1,2, Yajun Di1,2, Jinzhen Liu1,2

  • 1The School of Control Science and Engineering, Tiangong University, Tianjin 300387, People's Republic of China.

Physiological measurement
|October 18, 2023
PubMed
概括

一个新的3D自适应算法有效地消除了跨磁刺激与脑电图 (TMS-EEG) 记录相结合的脉冲器件. 这种方法显著提高了信号质量,并减少了大脑研究的处理时间.

关键词:
适应性算法 适应性算法一个电脑电图 (electroencephalogram) 是一个电脑电图.脉冲文物 人造物有理性的赫尔米特插值.跨的磁性刺激

更多相关视频

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
09:36

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

13.8K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

相关实验视频

Last Updated: Jul 13, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
08:23

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy

Published on: November 13, 2016

11.2K
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
09:36

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

13.8K
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
10:23

Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy

Published on: June 23, 2023

2.0K

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:

  • 跨磁刺激与脑电图 (TMS-EEG) 结合,对于研究大脑区域的反应性和连接性至关重要.
  • 由TMS电磁脉冲产生的脉冲工件显著污染了EEG信号,阻碍了准确的分析.
  • 为了推进TMS-EEG应用,有效和快速的工件清除是必不可少的.

研究的目的:

  • 开发和验证一种新的算法,以便在TMS-EEG中快速有效地去除脉冲器件.
  • 在速度和有效性方面解决现有文物清除技术的局限性.

主要方法:

  • 提出了一个三维自适应的理性二次 Hermite 插值算法.
  • 信号再组合创建了一个3D信号矩阵,使用衍生值识别的文物窗口.
  • 已识别的窗口是使用自适应式理性四进制Hermite插值算法进行插值的.

主要成果:

  • 拟议的算法显示了信号与噪声比 (SNR) 的显著改善,从23.88%到47.60%不等.
  • 根平均平方误差 (RMSE) 减少了46.52%至81.11%,平均绝对误差 (MAE) 减少了47.83%至58.33%.
  • 时间消耗减少了45.90%,相比零碎立方赫米特插值,表明提高了效率.

结论:

  • 拟议的3D自适应算法有效地快速删除TMS-EEG脉冲器件.
  • 与传统方法相比,这种方法在减少文物和处理速度方面提供了更高的性能.
  • 该算法可使用TMS-EEG.EEG进行更可靠,更有效的脑活动分析.