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

A lower-limb motor imagery BCI using virtual reality and novel calibration strategy in post-stroke patients.

Medical & biological engineering & computing·2026
Same author

Human-machine Interface using functional electrostimulation and inertial sensors for lower limb rehabilitation in spinal cord injury individuals: a proof of concept.

Medical & biological engineering & computing·2026
Same author

Cortical modulation through robotic gait training with motor imagery brain-computer interface enhances bladder function in individuals with spinal cord injury.

Scientific reports·2025
Same author

Markov chain-based computational model to assess user skills in sequential motor imagery tasks.

Computers in biology and medicine·2025
Same author

Rehabilitation of Chronic Stroke Using Neurofeedback, Functional Electrical Stimulation and Cerebrospinal Direct Current Stimulation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Lower-Limb Motor Imagery-Based Brain-Computer Interface to Control Treadmill Velocities.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025

相关实验视频

Updated: Jun 20, 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

43.3K

使用深度学习方法和EEG解读踏板任务期间下肢运动参数.

Cristian Felipe Blanco-Diaz1, Cristian David Guerrero-Mendez2, Rafhael Milanezi de Andrade3

  • 1Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil. cblanco88@uan.edu.co.

Medical & biological engineering & computing
|July 19, 2024
PubMed
概括

这项研究表明,深度学习可以从EEG信号中估计下肢运动,用于中风康复. 人工神经网络改善了机器人运动自行车的脑电脑界面控制.

关键词:
大脑与计算机接口 (BCI)卷积神经网络 (CNN) 是一种神经网络.运动重建的动态重建.长时间的短期记忆 (LSTM)下肢康复治疗 下肢康复治疗

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

862
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

15.1K

相关实验视频

Last Updated: Jun 20, 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

43.3K
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

862
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

15.1K

科学领域:

  • 神经科学是一个神经科学.
  • 机器人技术 机器人技术 机器人技术
  • 生物医学工程 生物医学工程

背景情况:

  • 脑卒中会损害运动能力,需要先进的康复工具,如脑电脑接口 (BCI).
  • 使用机器人系统与机器人系统的BCI恢复步行功能,例如电动化迷你运动自行车 (MMEB),显示出希望.
  • 从电脑电图 (EEG) 信号对连续运动进行准确的动力学估计仍然是一个重大挑战.

研究的目的:

  • 为了比较两个人工神经网络 (ANN) 解码器,用于估计踩踏过程中下肢动力学参数.
  • 评估使用深度学习 (DL) 在BCI控制的MMEB中进行连续解码的可行性.

主要方法:

  • 利用长短期记忆 (LSTM),一种循环神经网络 (RNN),来解码EEG特征.
  • 在踩踏任务时估计的脚位置 (x,y) 和膝关节角度.
  • 分析了踩踏速度和解码器性能之间的动力变异和相关性.

主要成果:

  • 从三角波段EEG特征进行动力参数重建,LSTM获得了大约0.58的皮尔森相关系数 (PCC).
  • 拟议的算法在区分踩踏和休息时间方面表现出有效性.
  • 在踏板速度和解码器性能之间观察到负线性相关性,这表明在较慢的速度下更容易估计.

结论:

  • 深度学习方法可用于估计踩踏过程中的EEG信号下肢动力学.
  • 这项研究促进了使用连续解码的BCI开发更强大的MMEB控制器.
  • 通过最大限度地提高BCI驱动系统的自由度,研究结果支持增强,个性化康复.