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

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Newton’s Method01:30

Newton’s Method

Newton’s Method is a powerful iterative technique for approximating the roots of real-valued, differentiable functions, particularly when analytical solutions are impractical. This approach is widely used in scientific computing, engineering, and finance, where equations may be too complex for traditional algebraic methods to handle. The method relies on an iterative process that refines an initial estimate using the function’s derivative to approach the true solution progressively.
Comparison Tests01:28

Comparison Tests

An infinite series composed of positive terms may either approach a finite value or increase without bound. Determining which outcome occurs is a central task in calculus, and comparison tests provide structured methods for making this determination. Rather than evaluating a series directly, these tests relate it to another series whose behavior is already known, allowing conclusions to be drawn through logical comparison.The direct comparison test applies to series with positive terms. If each...

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

Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition.

PLoS computational biology·2024
Same author

Workshops of the Eighth International Brain-Computer Interface Meeting: BCIs: The Next Frontier.

Brain computer interfaces (Abingdon, England)·2023
查看所有相关文章

相关实验视频

Updated: Jun 30, 2026

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

9.0K

脑与计算机接口算法的基准测试:里曼的方法与卷积神经网络的比较.

Manuel Eder1, Jiachen Xu1, Moritz Grosse-Wentrup1,2,3

  • 1Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria.

Journal of neural engineering
|July 25, 2024
PubMed
概括
此摘要是机器生成的。

深层卷积神经网络 (CNN) 和里曼的方法对脑计算机接口 (BCI) 显示了类似的解码性能. 这一发现为选择运动图像,P300和SSVEP范式的方法提供了灵活性.

关键词:
里曼的几何学里曼的几何学基准测试 (benchmarking) 是一种比较的方法.大脑-计算机接口接口这是分类分类的分类.卷积神经网络是一种卷积神经网络.电脑电图 (EEG) 是一种电脑电图.机器学习是机器学习.

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

861
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

1.2K

相关实验视频

Last Updated: Jun 30, 2026

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

9.0K
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

861
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

1.2K

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:

  • 大脑-计算机接口 (BCI) 通过神经信号实现通信和控制.
  • 对比深层卷积神经网络 (CNN) 和BCI的里曼解码方法对于推进该领域至关重要.
  • 现有的研究缺乏跨多种BCI范式的全面比较.

研究的目的:

  • 将新的CNN与BCI的最新里曼解码方法进行基准测试.
  • 为了评估跨运动图像,P300和稳定状态视觉唤起潜能 (SSVEP) 范式的性能.
  • 在会话内,跨会话和跨主题BCI设置中评估有效性.

主要方法:

  • 使用MOABB (所有BCI基准的母亲) 进行系统的评估.
  • 将EEGNet,浅层ConvNet和深层ConvNet与已建立的里曼解码技术进行比较.
  • 分析了使用会话内,跨会话和跨主题实验设计的解码性能.

主要成果:

  • 在CNN和Riemannian方法之间没有观察到解码性能的显著差异.
  • 在会议内部,跨会议和跨主题分析中,表现是可比的.
  • 这两种方法都在传统的BCI范式中表现出有效性.

结论:

  • 在许多场景中,CNN和里曼方法之间的选择可能不会对BCI解码性能产生重大影响.
  • 研究人员可以根据实施方便性,计算成本或偏好选择解码方法.
  • 这些发现支持BCI系统开发中的灵活性和实际考虑.