Jove
Visualize
联系我们

相关概念视频

Band Theory02:35

Band Theory

17.1K
When two or more atoms come together to form a molecule, their atomic orbitals combine and molecular orbitals of distinct energies result. In a solid, there are a large number of atoms, and therefore a large number of atomic orbitals that may be combined into molecular orbitals. These groups of molecular orbitals are so closely placed together to form continuous regions of energies, known as the bands.
The energy difference between these bands is known as the band gap.
Conductor, Semiconductor,...
17.1K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Nuclear Fusion02:45

Nuclear Fusion

33.7K
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
33.7K
Passive Filters01:27

Passive Filters

962
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
962
Crossing Over01:34

Crossing Over

169.3K
Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process...
169.3K
Active Filters01:25

Active Filters

1.3K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.3K

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

Lightweight Seizure Prediction Model based on Kernel-Enhanced Global Temporal Attention.

International journal of neural systems·2025
Same author

Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

International journal of neural systems·2024
Same author

Automatic seizure detection based on kernel robust probabilistic collaborative representation.

Medical & biological engineering & computing·2018
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
查看所有相关文章
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

相关实验视频

Updated: Jan 21, 2026

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.7K

通过动态多尺度交叉频段融合波器网络进行可解释的端到端扣留预测.

Jie Wang1, Yingchao Wang2, Weiwei Nie3

  • 1Shandong Key Laboratory of Medical Physics and Image Processing, School of Communication and Electronic Engineering, Shandong Normal University, Jinan 250358, P. R. China.

International journal of neural systems
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

一个新的AI模型,MCFNet,通过电脑电图 (EEG) 信号增强了发作预测. 它改善了特征表示和可解释性,为早期患者警告提供了一个有前途的工具.

关键词:
抢劫预测预测的预测跨频段融合注意事项 跨频段融合注意事项一个电脑电图 (electroencephalogram) 是一个电脑电图.可以解释的框架.同步光谱过网络是同步光谱过网络.

更多相关视频

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

7.0K

相关实验视频

Last Updated: Jan 21, 2026

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
13:18

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma

Published on: March 3, 2023

1.7K
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
06:14

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces

Published on: September 11, 2018

7.0K

科学领域:

  • 医学的人工智能 (AI)
  • 神经科学是一个神经科学.
  • 信号处理 信号处理

背景情况:

  • 使用脑电图 (EEG) 信号预测发作对患者的生活质量至关重要.
  • 现有的人工智能模型在不足的特征表示和有限的决策解释性方面扎.
  • 需要先进的模型,既具有高精度,又具有临床可解释性.

研究的目的:

  • 提出一个新的动态多尺度交叉带融合波器网络 (MCFNet),用于端到端的发作预测.
  • 解决目前基于EEG的预测模型中特征表示和可解释性的局限性.
  • 开发一种可行的方案,用于AI在预测中的临床应用.

主要方法:

  • 脑电图信号分解成多尺度组件,具有交叉频段融合注意力机制,用于信号融合.
  • 具有静态和动态模块的同步光谱过网络捕获周期组件和跨频道依赖关系.
  • 引入了联合特征可视化和特征剥离分析,以实现模型可解释性.

主要成果:

  • 在CHB-MIT数据集上,MCFNet实现了高性能:97.13%的灵敏度和97.22%的特异性.
  • 该模型显示了0.0326/h的低虚假阳性率 (FPR).
  • 实验结果证实了优异的预测性能和低的FPR,表明了临床可行性.

结论:

  • 在AI驱动的发作预测方面,MCFNet提供了显著的进步.
  • 该模型的增强特征表示和可解释性解决了该领域的关键挑战.
  • MCFNet为基于EEG的发作预测的现实世界临床应用提供了一个可行的解决方案.