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

EDSF-Net : An enhanced dynamic spatiotemporal-frequency attention network for robust EEG decoding in motor imagery.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Breaking the Depth Barrier in Motor Imagery Classification via a Residual Depthwise-Separable Network.

IEEE transactions on cybernetics·2026
Same author

Enhancing Target Recognition Performance in SSVEP-Based Brain-Computer Interfaces via Deep Neural Networks With Pyramid Squeeze Attention.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

BR-SFDA: A Source-Target Bidirectional Refined SFDA for Privacy Preserving EEG-based BCIs.

IEEE journal of biomedical and health informatics·2026
Same author

Enhancing the Capability and Accuracy of Motor Imagery Classification: A Deep Neural Network-Powered Multifaceted Strategy Model.

IEEE transactions on cybernetics·2026
Same author

TBMSCCN: Two-Branch Multi-Scale Convolutional Correlation Network for Steady-State Visual Evoked Potential Classification.

IEEE transactions on bio-medical engineering·2026

相关实验视频

Updated: May 16, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

792

用EEG信号检测上肢运动轨迹的可解释回归方法

Miao Tian, Shurui Li, Ren Xu

    IEEE transactions on bio-medical engineering
    |April 2, 2025
    PubMed
    概括

    这项研究引入了一种使用脑电图 (EEG) 预测上肢运动轨迹的新脑电脑接口 (BCI) 方法. 该方法确定了EEG关键特征,以改善运动残疾人的假肢控制.

    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
    • 康复技术 康复技术 康复技术

    背景情况:

    • 使用脑电图 (EEG) 的脑电脑接口 (BCI) 对于开发先进的假肢设备至关重要.
    • 现有的研究往往忽视了EEG频段特征和肢体动力学之间的关系.
    • 准确的运动轨迹预测 (MTP) 对于恢复残疾人的运动功能至关重要.

    研究的目的:

    • 为了确定关键的脑电图通道和频段,用于上肢运动预测.
    • 开发一个可解释的框架,从EEG信号中重建3D运动轨迹.
    • 为了提高电机辅助设备的BCI的性能.

    主要方法:

    • 从多个EEG频段中提取带功率特征.
    • 将特征连接到多频段融合表示中.
    • 极端梯度增强回归的应用与贝叶斯优化和莎普利的附加解释解释性.

    主要成果:

    • 拟议的方法实现了0.452.45的平均皮尔森相关系数 (PCC).
    • 与传统回归模型相比,表现出优越的性能.
    • 确定了对右手运动至关重要的特定EEG通道 (Mu频段的C5,Beta频段的C3).

    更多相关视频

    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

    43.2K
    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
    11:06

    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

    Published on: April 12, 2016

    10.4K

    相关实验视频

    Last Updated: May 16, 2025

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
    06:37

    Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

    Published on: July 14, 2023

    792
    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

    43.2K
    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
    11:06

    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

    Published on: April 12, 2016

    10.4K

    结论:

    • 反侧脑半球为运动轨迹回归提供了更重要的信息.
    • 开发的框架提高了MTP模型的清晰度和可解释性.
    • 这项研究为全面研究运动障碍提供了一种全新的方法.