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

Development of a Double-Antibody Sandwich ELISA for the Detection of HPV16 E6 Protein.

Diagnostics (Basel, Switzerland)·2026
Same author

Drought Severity and Nitrogen Addition Interactively Modulate Seedling Growth and Resource-Use Strategies of <i>Quercus wutaishanica</i>.

Biology·2026
Same author

Multi-Trait Genetic Insights Into Schizophrenia Across Ancestries: Genome-Wide Association Meta-Analyses, Machine Learning, and Drug Repurposing Study.

Brain and behavior·2026
Same author

Loss of PDL1 in mesenchymal stromal cells from young-onset MDS-RAEB-1 promotes inflamm-aging through epigenetic dysregulation of H3K9me3.

Stem cells translational medicine·2026
Same author

enCas7-11S3: A compact Cas7-11 variant with enhanced RNA cleavage and minimal collateral activity.

International journal of biological macromolecules·2026
Same author

Halicin-loaded injectable hyaluronic acid hydrogel for ferroptosis-driven osteosarcoma therapy via Fe<sup>2+</sup> accumulation.

International journal of biological macromolecules·2026

相关实验视频

Updated: Jun 25, 2025

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
00:08

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

14.3K

准备性运动状态增强了脑电脑接口的运动前EEG表示.

Yuxin Zhang1,2,3, Mengfan Li1,2,3, Haili Wang1,2,3

  • 1School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, People's Republic of China.

Journal of neural engineering
|May 28, 2024
PubMed
概括

将准备状态集成到脑计算机接口 (BCI) 范式中,可以显著改善运动意图检测. 这种增强的BCI编码通过在前移动期间提炼神经信号来提高性能,使基于运动的BCI更有效.

关键词:
大脑 计算机接口编码范式的编码范式提前移动的意图 提前移动意图预备运动状态 预备运动状态

更多相关视频

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K
Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
08:09

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

10.9K

相关实验视频

Last Updated: Jun 25, 2025

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation
00:08

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

14.3K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.0K
Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
08:09

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

10.9K

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:

  • 运动相关的大脑与计算机接口 (BCI) 提供了多种应用,特别是在检测预移动意图方面.
  • 目前的BCI面临挑战,因为在前移动和注意力干扰期间,电脑脑电图 (EEG) 功能模糊,限制了性能.
  • 提高运动意图的检测对于推进基于运动的BCI至关重要.

研究的目的:

  • 开发和验证一个新的BCI编码范式,该范式整合了预备运动状态.
  • 通过结合准备信号来提高基于电机的BCI中运动意图的检测准确度.
  • 为了比较预先准备和自发预移的神经特征.

主要方法:

  • 两个按任务旨在引起预先准备好的左/右移动意图,与自发的预移动形成鲜明对比.
  • 记录和分析了14名受试者的低频运动相关皮质潜力 (MRCP) 和高频事件相关脱同步 (ERD) EEG数据.
  • 化提取了MRCP和ERD特征,然后使用通用空间模式 (CSP) 算法对它们进行分类.

主要成果:

  • 与自发预移相比,预备的预移表现出较低的MRCP振幅和较早的延迟,并注意到主导手的影响.
  • 频域分析显示ERD值较低,ERD恢复速度较快,预备的预移.
  • 融合方法使分类精度从78.92% (自发) 提高到83.59% (准备) (p<0.05),标准偏差减少.

结论:

  • 整合一个准备状态可以增强神经对运动的表现,显著改善基于运动的BCI性能.
  • 拟议的编码范式有效地提高了对运动意图的检测.
  • 这种方法有可能在BCI中解码更广泛的运动意图和相关的神经信息.