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

[Multidimensional clinical phenotypes of obstructive sleep apnea hypopnea syndrome based on unsupervised clustering algorithm].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026
Same author

Glutamate-cysteine ligase catalytic related glutathione metabolism inhibited high mobility group box 1 release by regulating epithelium ferroptosis in asthma.

Journal of thoracic disease·2026
Same author

Decarboxylative and Deoxygenative/Decarbonylative Alkylation of Heterocycles with α-Keto Acids.

Organic letters·2026
Same author

Relationship between obstructive sleep apnea and distribution of epicardial adipose tissue.

European journal of medical research·2026
Same author

Mild Photothermal Stimulation Driven Nanoparticles Hybrid Dual-Network Hydrogels for Bone Repair.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Correlation between obstructive sleep apnea and pituitary function in pituitary adenomas patients.

Respiratory medicine·2026

相关实验视频

Updated: Jul 23, 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

叠加过器银行卷积神经网络用于多主体多类别的运动图像大脑计算机接口.

Jing Luo1,2, Jundong Li3,4, Qi Mao3,4

  • 1Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi, People's Republic of China. luojing@xaut.edu.cn.

BioData mining
|July 11, 2023
PubMed
概括

这项研究引入了一个重叠过器银行卷积神经网络 (CNN),通过利用多个EEG频段进行运动图像识别来提高脑计算机接口 (BCI) 的性能. 这种新的方法提高了多主题BCI应用中的准确性和区分特征.

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.运动图像 (MI)多学科的BCI是多学科的BCI.叠加过器银行叠加过器

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
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

15.2K

相关实验视频

Last Updated: Jul 23, 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
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

15.2K

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:

  • 运动图像大脑-计算机接口 (BCI) 对大脑-计算机集成至关重要.
  • 电脑脑脑电图操作频段对运动图像识别模型的性能有很大影响.
  • 当前的算法往往忽略了来自多个EEG子频段的歧视性信息.

研究的目的:

  • 开发一种使用卷积神经网络 (CNN) 进行多主体运动图像识别的新方法.
  • 充分利用多个EEG频率组件的区分特征.
  • 为了提高运动图像BCI的性能.

主要方法:

  • 提出了一个新的重叠过器银行CNN框架.
  • 两个重叠的过器银行 (固定和滑动低切频) 提取了多个EEG频率表示.
  • 多个CNN模型被单独训练,他们的输出被整合用于预测.

主要成果:

  • 叠加过器银行CNN在提高多主体运动图像BCI性能方面展示了效率和通用性.
  • 与原来的骨干模型相比,平均精度增加了3.69%,F1得分增加了0.04,AUC增加了0.03.
  • 拟议的方法优于最先进的方法.

结论:

  • 拟议的重叠过器银行 (CNN) 框架,特别是具有固定的低切割频率,提供了一个高效和通用的解决方案.
  • 这种方法有效地提高了多主体运动图像BCI性能.
  • 该方法成功地利用了多个EEG频率组件的区分特征.