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

Sensing interfaces for human-robot interaction: a scoping review for robotic walkers.

Disability and rehabilitation. Assistive technology·2026
Same author

A case report on gait in spinocerebellar ataxia: evaluating the use of mixed reality from independent walking to integrated physical support.

Disability and rehabilitation. Assistive technology·2026
Same author

Quantifying Slowness in Parkinson Disease Using a Serious Game: Cross-Sectional Study.

JMIR serious games·2026
Same author

Towards Biomimetic Robotic Rehabilitation: Pilot Study of an Upper-Limb Cable-Driven Exoskeleton in Post-Stroke Patients.

Biomimetics (Basel, Switzerland)·2026
Same author

Authors' Reply: Is the Pinball Machine a Blind Spot in Serious Games Research?

JMIR serious games·2025
Same author

Atomic force microscopy lateral force calibration using a V-shape scratch made by a nanoindenter.

The Review of scientific instruments·2025

相关实验视频

Updated: Jul 26, 2025

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.3K

使用深度学习方法对原始BCI用户进行EEG运动图像分类.

Cristian D Guerrero-Mendez1, Cristian F Blanco-Diaz1, Andres F Ruiz-Olaya2

  • 1Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil.

Biomedical physics & engineering express
|June 15, 2023
PubMed
概括

深度学习方法显著提高了新用户的大脑计算机接口 (BCI) 的性能. 该LSTM-BiLSTM方法实现了80%的准确性,增强了机器人设备的BCI控制.

关键词:
BCI的文盲问题深度学习 (Deep Learning) 是一种深度学习.这是一个EEGEEGEEGEEGEEGEEGEEG.新的方法 新的方法用户体验用户体验

更多相关视频

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.0K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K

相关实验视频

Last Updated: Jul 26, 2025

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

1.3K
Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.0K
Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

9.1K

科学领域:

  • 神经科学是一个神经科学.
  • 计算机科学 计算机科学
  • 生物医学工程 生物医学工程

背景情况:

  • 运动图像 (MI) -脑计算机接口 (BCI) 文盲会影响用户的性能,原因包括疲劳和缺乏经验.
  • 纯粹的BCI用户往往难以实现最佳的系统性能.

研究的目的:

  • 调查三种深度学习 (DL) 方法在提高BCI性能方面对原始用户的有效性.
  • 将DL方法与MI信号歧视的传统基线方法进行比较.

主要方法:

  • 卷积神经网络 (CNN),长期短期记忆 (LSTM) /双向长期短期记忆 (BiLSTM) 和CNN-LSTM模型的实施.
  • 对25名先验BCI用户的数据集进行了评估,以对上肢MI信号进行歧视.
  • 与基线方法的比较:通用空间模式 (CSP),过器银行通用空间模式 (FBCSP) 和过器银行通用空间频谱模式 (FBCSSP).

主要成果:

  • LSTM-BiLSTM方法表现出卓越的性能,平均准确度为80% (高达95%) 和信息传输速率 (ITR) 为10比特/分钟,时间窗口为1.5秒.
  • 与基线方法相比,DL方法显示了显著的32%的性能增加 (p<0.05).
  • 关键性能指标包括准确性,F-score,回忆,特异性,精度和ITR.

结论:

  • 深度学习方法,特别是LSTM-BiLSTM,对于缺乏经验的用户来说,大大提高了BCI系统的性能.
  • 这些发现表明,对于原始的BCI用户来说,机器人设备的可控性,可用性和可靠性增加了.
  • 这项研究为更容易获得和更有效的BCI应用铺平了道路.