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

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used.
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:

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

VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral imagery.

Plant phenomics (Washington, D.C.)·2026
Same author

Injectable Thermal-Protective Hydrogel Enables Curative Tumor Ablation via Chemo-Immunomodulation.

ACS applied materials & interfaces·2026
Same author

Primary diffuse large B-cell lymphoma of the rectus abdominis muscle: a presumed primary case report and literature review.

Frontiers in oncology·2026
Same author

State-dependent repetitive transcranial magnetic stimulation in disorders of consciousness.

Frontiers in human neuroscience·2026
Same author

Machine learning-based identification of basement membrane-related signature to predict recurrence and immunotherapy benefit in bladder cancer.

Immunologic research·2026
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026
Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026
Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026
Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026
Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026
Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K

一个基于SSVEP的BCI与112个目标使用频率空间复杂化.

Yaru Liu1, Wei Dai1, Yadong Liu1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410000, People's Republic of China.

Journal of neural engineering
|April 19, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于大脑计算机接口 (BCI) 的新型频率空间复杂化方法,以增加目标分辨率. 该方法使用图形神经网络来实现稳定状态视觉唤起潜能 (SSVEP) 检测的高精度.

关键词:
大脑计算机接口 (BCI)电脑电图 (EEG) 是一种电脑电图.频率空间复杂化频率空间复杂化图形神经网络 (GNN) 是指图形神经网络.稳定状态视觉唤起潜力 (SSVEP)

更多相关视频

Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex
07:42

Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex

Published on: August 17, 2018

11.8K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.3K

相关实验视频

Last Updated: Jun 23, 2026

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K
Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex
07:42

Combined Transcranial Magnetic Stimulation and Electroencephalography of the Dorsolateral Prefrontal Cortex

Published on: August 17, 2018

11.8K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

2.3K

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:

  • 脑计算机接口 (BCI) 系统在实现高目标分辨率方面面临挑战,这限制了它们的实际应用.
  • 基于稳态视觉唤起潜力 (SSVEP) 的BCI为众多目标提供了潜力,但受到刺激竞争的阻碍.
  • 改进目标解决方案对于推进BCI能力和满足应用需求至关重要.

研究的目的:

  • 在基于SSVEP的BCI中克服刺激竞争的局限性.
  • 提出和验证频率空间复杂化方法,以提高目标分辨率.
  • 通过提高可访问命令的数量来提高BCI系统的性能.

主要方法:

  • 通过将闪刺激安排为2x2矩阵在接口中,开发了一种频率空间复杂化范式.
  • 使用拟议的范式设计和测试了三个不同的界面布局.
  • 实现了一个图形神经网络,以根据EEG响应模式区分相同频率的目标.

主要成果:

  • 11名受试者的实验验证表明了拟议方法的有效性.
  • 在三个范式中,线下分类的平均准确率高达91.38%.
  • 实现了高信息传输速率 (ITR),最高的信息传输速率为53.96比特/分钟.

结论:

  • 频率空间复杂化方法通过利用刺激空间关系,成功地增加了SSVEPBCI中的目标分辨率.
  • 开发的图形神经网络有效地区分目标,提高了分类准确性.
  • 这种方法为SSVEP检测效率和BCI性能进一步提高提供了基础.