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

Shengxian decoction suppresses malignant progression of lung adenocarcinoma by enhancing CD8<sup>+</sup> T cell function via the FYN-PI3K/AKT axis.

Chinese medicine·2026
Same author

Mapping the genetic landscape of the DNA damage response with Cas12a-based combinatorial knockout screens.

bioRxiv : the preprint server for biology·2026
Same author

High-Throughput Olink Proteomics Elucidates the Immuno-Neurological Landscape of Gastric Cancer.

Journal of proteome research·2026
Same author

TFPI2 in tumor metastasis: a double-edged sword with clinical implications.

Cancer biology & therapy·2026
Same author

The "oral-gut axis" transmission of microorganisms in colorectal cancer: Insights from <i>Peptostreptococcus'</i> perspective.

Virulence·2026
Same author

Metagenomic sequencing reveals the similarities and differences in microbial community structure and diversity between fermented whey and Rubing cheese, a fresh goat milk cheese.

Food research international (Ottawa, Ont.)·2026

相关实验视频

Updated: Jul 8, 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

43.4K

基于对EEG和EMG之间的监督对比学习的步行模式识别.

Xi Fu, Cuntai Guan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种使用电脑图 (EEG) 和电肌图 (EMG) 信号进行改善下肢动力学分析的多式训练策略. 该方法提高了测试期间仅使用EEG的步态预测准确度.

    更多相关视频

    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

    10.5K
    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

    8.5K

    相关实验视频

    Last Updated: Jul 8, 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

    43.4K
    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

    10.5K
    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

    8.5K

    科学领域:

    • 生物医学工程 生物医学工程
    • 神经科学是一个神经科学.
    • 康复技术 康复技术 康复技术

    背景情况:

    • 电脑图 (EEG) 和下肢电肌图 (EMG) 对于下肢运动任务至关重要.
    • 虽然EMG提供了更高的准确性,但也面临着诸如疲劳和信号采集困难等挑战.
    • 脑电图信号是稳定的,更容易获得,为步态分析提供了替代方案.

    研究的目的:

    • 开发一种利用监督对比学习的多式模式培训策略.
    • 为了提高下肢动力学分类和回归使用EEG信号.
    • 通过在培训期间使用EMG作为指南来提高步态分析的准确性.

    主要方法:

    • 提出了一种采用监督对比学习的多式模式培训策略.
    • 利用EMG信号指导模型的训练阶段进行步态分析.
    • 雇佣的EEG信号仅在测试阶段进行评估.

    主要成果:

    • 多模式策略与单模式EEG培训相比,表现优越.
    • 用拟议的策略训练的模型在下肢动力学任务中获得了更高的准确性.
    • 皮尔森相关系数超过了所有基线模型的相关系数,验证了该方法.

    结论:

    • 拟议的多式联络培训战略有效地利用了EMG在基于EEG的步态分析中的指导作用.
    • 这种方法克服了EMG采集的局限性,同时实现了强大的动力学预测.
    • 这些发现表明了改善非侵入性步行监测系统的有希望的方向.