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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.8K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.8K

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

An online brain-computer interface for detecting incongruity in augmented reality applications.

Journal of neural engineering·2026
Same author

Turning motor intentions into words: an MRCP-based BCI speller for motor-impaired users enhanced by task-specific calibration.

Journal of neural engineering·2026
Same author

Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.

PLoS computational biology·2026
Same author

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Source localization of simulated neural signals in a cervical spinal cord model.

Journal of neural engineering·2026
Same author

A Non-Invasive, MRCP-Based BCI for Online Communication.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Motion Intention Recognition and DDPG-Based Adaptive Impedance Control for a Robotic Upper-Limb Exoskeleton.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

CNN-Based Modelling Reveals Temporal Brain Dynamics of Auditory Intensity Processing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Pathology-Informed Augmentation Improves Cross-Cohort IMU-to-vGRF Estimation Between Healthy Adults and Adults With Osteoarthritis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Effects of task-driven head orientations on gait and balance during walking in virtual reality.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Wearable sensor-based Mild Cognitive Impairment Identification: A Multi-Domain Gait Analysis Approach with Association Rule Mining.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
查看所有相关文章

相关实验视频

Updated: Jan 8, 2026

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.0K

EEG2GAIT:基于EEG的步行解码的层次图形卷积网络.

Xi Fu, Rui Liu, Aung Aung Phyo Wai

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |December 22, 2025
    PubMed
    概括
    此摘要是机器生成的。

    研究人员开发了EEG2GAIT,这是一种使用层次图形网络和混合损失函数来解码EEG信号的步态动态的新型模型. 这种方法显著提高了大脑与计算机接口在康复和辅助技术中的准确性.

    更多相关视频

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    548
    Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
    06:25

    Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

    Published on: August 12, 2019

    9.0K

    相关实验视频

    Last Updated: Jan 8, 2026

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    44.0K
    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    548
    Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
    06:25

    Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

    Published on: August 12, 2019

    9.0K

    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
    • 机器学习 机器学习

    背景情况:

    • 从脑电图 (EEG) 信号中解码步态动态是具有挑战性的,因为复杂的运动过程和有限的高质量数据集.
    • 精确的时间和光谱特征提取对于可靠的步态解码至关重要.

    研究的目的:

    • 引入EEG2GAIT,这是一个基于图形的新型层次模型,用于基于EEG的增强步行解码.
    • 通过混合时光光谱奖励 (HTSR) 损失函数来提高解码性能.
    • 为推动该领域的研究提供新的步行EEG数据集 (GED).

    主要方法:

    • 使用层次图形卷积网络 (GCN) 金字塔来捕捉EEG通道的多层空间嵌入.
    • 开发了一个混合时光谱奖励 (HTSR) 损失函数,集成时间域,频域和基于奖励的组件.
    • 收集并同步了一个新的步行EEG数据集 (GED) 与50名参与者的下肢关节角度数据.

    主要成果:

    • 使用HTSR的EEG2GAIT在GED数据集上实现了高性能 (r=0.959,R2=0.914,MAE=0.193).
    • 在MoBI数据集上表现优于现有方法 (r=0.779,R2=0.597,MAE=4.384).
    • 移除和突出性研究验证了模型的组件,并突出显示了与运动相关的大脑区域的参与.

    结论:

    • 带有HTSR的EEG2GAIT在解码EEG信号的步态动态方面表现出卓越的性能.
    • 该模型显示了大脑-计算机接口应用程序的重大潜力,特别是在下肢康复中.
    • 开发的数据集和模型为未来的步态分析和BCI研究提供了宝贵的资源.