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

Electro-mechanical Systems01:19

Electro-mechanical Systems

Electromechanical systems are intricate configurations that effectively combine electrical and mechanical elements to achieve a desired outcome. Central to many of these systems is the DC motor, a device that converts electrical energy into mechanical motion, enabling various applications ranging from simple fans to complex robotic mechanisms.
A key component of the DC motor is the armature, a rotating circuit positioned within a magnetic field. As an electric current passes through the...

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

CRISPR/Cas9 in locusts: Successful establishment of an olfactory deficiency line by targeting the mutagenesis of an odorant receptor co-receptor (Orco).

Insect biochemistry and molecular biology·2016
Same author

To Be or Not To Be Humorous? Cross Cultural Perspectives on Humor.

Frontiers in psychology·2016
Same author

Armadillo Repeat-Containing Protein 8 (ARMC8) Silencing Inhibits Proliferation and Invasion in Osteosarcoma Cells.

Oncology research·2016
Same author

Knockdown of DDX46 Inhibits the Invasion and Tumorigenesis in Osteosarcoma Cells.

Oncology research·2016
Same author

Corrigendum: The Associations of Dyadic Coping and Relationship Satisfaction Vary between and within Nations: A 35-Nation Study.

Frontiers in psychology·2016
Same author

Changes in c-Kit expression levels during the course of radiation therapy for nasopharyngeal carcinoma.

Biomedical reports·2016
Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026
Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 15, 2026

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
10:48

How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

Published on: June 3, 2013

22.2K

磁脑学解码转移方法:从深度学习模型到内在可解释模型

Yongdong Fan, Qiong Li, Haokun Mao

    IEEE journal of biomedical and health informatics
    |February 13, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的方法,使磁脑电图 (MEG) 解码的深度学习模型可解释. 该方法将知识从复杂的深度模型转移到更简单,可解释的模型,提高准确性和理解性.

    更多相关视频

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.4K
    Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
    09:25

    Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

    Published on: July 26, 2019

    6.9K

    相关实验视频

    Last Updated: Jun 15, 2026

    How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
    10:48

    How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging

    Published on: June 3, 2013

    22.2K
    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
    11:25

    Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

    Published on: July 26, 2013

    43.4K
    Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
    09:25

    Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

    Published on: July 26, 2019

    6.9K

    科学领域:

    • 神经科学是一个神经科学.
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 深度学习模型擅长解码神经电生理信号,如磁脑摄影 (MEG),但缺乏可解释性.
    • 这种解释性差距阻碍了在现实场景中的可靠性和伦理应用.
    • 内在可解释的模型提供了透明度,但往往会损害预测准确性.

    研究的目的:

    • 开发一种方法,将深度学习的高精度与MEG解码的简单模型的可解释性相结合.
    • 为了使复杂的深度模型转化为准确和内在可解释的模型.

    主要方法:

    • 开创了一种MEG转移方法,使用基于特征归属的知识蒸.
    • 首次引入后期特征知识,特别是特征归属地图,用于知识蒸.
    • 引导内在可解释模型 (学生) 从深度学习模型 (教师) 吸收知识.

    主要成果:

    • 提出的方法显著超过了基准知识蒸算法.
    • 软决策树的预测准确度提高了8.28%.
    • 结果的可解释模型显示性能与深度教师模型相比或优于深度教师模型.

    结论:

    • 基于特征归属的知识蒸有效地传输MEG解码信息,产生可解释但非常准确的模型.
    • 这种不依赖模型的方法为提高神经成像数据分析的可靠性提供了广泛的应用.
    • 该方法在复杂的神经科学机器学习应用中弥合了预测性能和可解释性之间的差距.