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

Regulatory measures for mitigating physical and mental health impacts in aerospace environment: A systematic review.

Life sciences in space research·2025
Same author

Dataset of binocularly coded steady-state visual evoked potentials recorded with an augmented reality headset.

Scientific data·2025
Same author

Adaptive Neurofeedback Training Using a Virtual Reality Game Enhances Motor Imagery Performance in Brain-Computer Interfaces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Corrigendum to "Sendai virus-based immunoadjuvant in hydrogel vaccine intensity-modulated dendritic cells activation for suppressing tumorigenesis" [Bioact. Mater. 6 (2021) 3879-3891].

Bioactive materials·2025
Same author

Enhanced theta oscillations in the left temporoparietal region associated with refractory positive symptoms in schizophrenia.

Schizophrenia (Heidelberg, Germany)·2025
Same author

Cortical changes induced by increased cognitive task difficulty during dual task balancing correlate with postural instability in elders and patients with Parkinson's disease.

Journal of neural engineering·2025

相关实验视频

Updated: May 24, 2025

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

使用超高密度EEG解码手臂运动方向

Zhen Ma, Xinyi Yang, Jiayuan Meng

    IEEE journal of biomedical and health informatics
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种超高密度电脑电图 (EEG) 系统来解码多向手臂运动. 这种新的系统在区分手臂运动方面实现了高精度,为大脑与计算机接口 (BCI) 提供了新的可能性.

    更多相关视频

    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

    798
    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
    13:32

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

    Published on: June 26, 2012

    25.7K

    相关实验视频

    Last Updated: May 24, 2025

    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
    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

    798
    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
    13:32

    Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

    Published on: June 26, 2012

    25.7K

    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
    • 康复技术 康复技术 康复技术

    背景情况:

    • 解码手臂运动方向对于恢复运动残疾人的自我护理至关重要.
    • 侵入性脑电脑接口 (BCI) 是有前途的,但传统的脑电图 (EEG) 却难以处理多向手臂运动解码.
    • 现有的基于EEG的BCI在有效地解释复杂的手臂运动方面面临挑战.

    研究的目的:

    • 开发和验证一个超高密度 (UHD) 的EEG系统来解码多向手臂运动.
    • 分析与不同手臂运动方向相关的UHDEEG信号模式.
    • 引入一种新的空间过方法,用于从UHDEEG数据中提取增强的特征.

    主要方法:

    • 设计了一种超高密度 (UHD) 的EEG系统,在4毫米间隔内设有200个电极.
    • 在手臂运动期间分析了脑电图 (EEG) 信号模式,特别是与运动相关的皮质潜力 (MRCPs).
    • 采用空间过技术,将主要组件分析 (PCA) 和区分空间模式 (DSP) 结合起来,用于特征提取.

    主要成果:

    • 证明了UHDEEG信号在波形和空间模式上的可分离性,用于不同的手臂运动方向.
    • 在双臂的八个方向运动中,平均分类准确率为63.15%,峰值为77.24%.
    • 在主导臂的四向运动中获得了75.31%的平均准确度,达到85.00%的峰值.

    结论:

    • 这项研究成功地使用UHDEEG解码了多向手臂运动,这是第一次同时进行双边手臂解码.
    • 开发的UHDEEG系统和空间过方法为先进的BCI提供了有前途的方法.
    • 这些发现对开发用于上肢运动障碍患者的辅助技术具有重大意义.