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

EDTA: Auxiliary Complexing Reagents01:26

EDTA: Auxiliary Complexing Reagents

1.4K
EDTA titrations are usually carried out in highly basic conditions, where the fully deprotonated form of EDTA, Y4−, actively complexes with the free metal ions in the solution. Several metal ions precipitate as hydrous oxide (hydroxides, oxides, or oxyhydroxides) under these conditions, lowering the concentration of free metal ions in the solution. For this reason, auxiliary complexing agents or ligands such as ammonia, tartrate, citrate, or triethanolamine are used in EDTA titrations to...
1.4K
Oxidation-Reduction Reactions03:11

Oxidation-Reduction Reactions

75.9K
Oxidation–Reduction Reactions
75.9K
Force Classification01:22

Force Classification

2.5K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.5K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.4K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.4K
Classification of Leukocytes01:30

Classification of Leukocytes

6.2K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
6.2K
Classification of Illness01:17

Classification of Illness

8.9K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.9K

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

Acquisition and Analysis of Facial Electromyographic Signals for Emotion Recognition.

Sensors (Basel, Switzerland)·2024
Same author

Attenuation of initial pilocarpine-induced electrographic seizures by methionine sulfoximine pretreatment tightly correlates with the reduction of extracellular taurine in the hippocampus.

Epilepsia·2023
Same author

Registration and Analysis of Acceleration Data to Recognize Physical Activity.

Journal of healthcare engineering·2019
Same author

System for automatic heart rate calculation in epileptic seizures.

Australasian physical & engineering sciences in medicine·2017

相关实验视频

Updated: Feb 14, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.8K

通过使用辅助传感器进行EEG人工物减少来改进SSVEP分类.

Marcin Kołodziej1, Andrzej Majkowski1, Przemysław Wiszniewski1

  • 1Faculty of Electrical Engineering, Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
概括

这项研究通过使用辅助通道减少脑电图 (EEG) 装置,提高了脑电脑接口 (BCI) 的性能. 移除文物增加了分类准确性,提高了BCI对现实应用的可靠性.

关键词:
这就是BCI的意义.这是一个EEGEEGEEGEEGEEGEEGEEG.在EMGEMGEMGEMGEMGEMGEMGEMGEM经济发展组织 (EOG) 是一个经济发展组织.这是SSVEP的SSVEP.文物消除 文物消除减少人工制造物的减少.的辅助传感器.

更多相关视频

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

13.8K
Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor
06:19

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

Published on: January 12, 2018

9.5K

相关实验视频

Last Updated: Feb 14, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.8K
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

13.8K
Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor
06:19

Visual Evoked Potential Recordings in Mice Using a Dry Non-invasive Multi-channel Scalp EEG Sensor

Published on: January 12, 2018

9.5K

科学领域:

  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程
  • 信号处理 信号处理

背景情况:

  • 稳态视觉唤起潜能 (SSVEP) 对脑计算机接口 (BCI) 至关重要.
  • 脑电图 (EEG) 信号在BCI中经常被肌肉,运动和眼睛运动的工件损坏.
  • 这些器件显著降低了BCI的性能,特别是在肌肉紧张度高或非自愿的眼动的人群中.

研究的目的:

  • 开发和评估用于脑电脑接口 (BCI) 系统的电脑脑图 (EEG) 人工物减少方法.
  • 通过减轻工件来提高SSVEP的信号质量和分类准确性.
  • 为了确定最有效的辅助道来抑制文物.

主要方法:

  • 利用辅助通道 (中央,正面,电眼镜,部,脸,下巴) 来建模干扰源.
  • 应用线性回归在1秒窗口内用于EEG信号清理.
  • 执行频域分析并使用支持矢量机 (SVM) 和卷积神经网络 (CNN) 算法进行SSVEP分类.
  • 在受控的工件生成和视觉刺激期间记录的数据在7,8和9赫兹.

主要成果:

  • 在移除文物后,分类准确度提高了9%.
  • 确定中央 (Cz) 和下通道对人工物抑制最为重要.
  • 显示了EEG信号质量和BCI可靠性的显著改善.

结论:

  • 辅助通道有效地减少了基于SSVEP的BCI中的EEG工件.
  • 开发的人工物减少方法提高了BCI系统的性能和可靠性.
  • 这种方法对现实世界中的BCI应用具有更好的信号完整性的承诺.