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

Mapping health literacy challenges among COPD caregivers: a scoping review.

Frontiers in public health·2026
Same author

Matrix metalloproteinase-12 in arterial diseases: context-dependent mechanisms of vascular remodeling and therapeutic implications.

Frontiers in cardiovascular medicine·2026
Same author

Modulation of Exciton Transport in Few-Layer and Bulk Tungsten Disulfide under Hydrostatic Pressure.

Langmuir : the ACS journal of surfaces and colloids·2026
Same author

TRIM21-mediated ubiquitination of PARP1 regulated by the PI3K/AKT-STAT5A axis suppresses small cell lung cancer.

Nature communications·2026
Same author

Crosstalk between iron metabolism dysregulation and the oral microbiome in periodontitis.

Journal of oral microbiology·2026
Same author

Intravenous human umbilical cord-derived mesenchymal stem cell transplantation for ischemic stroke (MERIT): A phase 1 trial.

Cell reports. Medicine·2026

相关实验视频

Updated: Jan 10, 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

基于EEG的运动解码在运动障碍患者通过提取和调整神经模式与健康个体.

Jiarong Wang, Luzheng Bi, Yuyang Wei

    IEEE journal of biomedical and health informatics
    |November 26, 2025
    PubMed
    概括

    本研究介绍了TL-ME,一种转移学习模型,通过利用健康个体的数据,提高了运动障碍患者的大脑计算机接口 (BCI) 的准确性. 这可以增强神经解码,用于神经康复应用.

    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:

    • 大脑-计算机接口 (BCI) 对神经康复至关重要,但由于数据收集困难和模型通用性问题,它面临着运动障碍患者的挑战.
    • 与健康个体相比,现有的BCI模型很难适应患者独特的脑功能结构和运动行为.

    研究的目的:

    • 开发一种新的转移学习模型,TL-ME,以提高运动障碍患者的运动解码精度.
    • 弥合健康个体和患者之间的数据差距,以改善神经康复中的BCI性能.

    主要方法:

    • 拟议的TL-ME模型整合了基于注意力的特征提取,对抗性域区分,多源选择和运动分类器.
    • 利用转移学习将知识从健康个体的脑电图 (EEG) 数据 (源域) 转移到患者的EEG数据 (目标域).
    • 使用时间和光谱可视化来分析共享的运动任务大脑激活模式.

    主要成果:

    • 在使用TL-ME模型的运动障碍患者的上肢运动解码精度得到了10.8%的改善.
    • 在TL-ME框架内,每个模块都取得了显著的绩效增长.
    • 视觉化分析证实了健康个体和患者之间类似的大脑激活模式,验证了跨人群数据的可转移性.

    结论:

    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

    1.3K
    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
    11:31

    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

    Published on: December 5, 2014

    15.6K

    相关实验视频

    Last Updated: Jan 10, 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
    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

    1.3K
    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
    11:31

    Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

    Published on: December 5, 2014

    15.6K
    • TL-ME模型提供了一种新的跨人群转移学习方法,用于增强基于BCI的神经康复中的神经解码.
    • 成功地利用健康个体的EEG数据来改进患者特定的模型,解决当前BCI研究的关键局限性.
    • 这项工作有助于将BCI技术从实验环境转化为动力受损人群的现实应用.