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

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

FedNolowe: A normalized loss-based weighted aggregation strategy for robust federated learning in heterogeneous environments.

PloS one·2025
Same author

The impact of data imputation on air quality prediction problem.

PloS one·2024
Same author

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

IEEE journal of translational engineering in health and medicine·2024
Same author

Physically Meaningful Surrogate Data for COPD.

IEEE open journal of engineering in medicine and biology·2024
Same author

Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller.

Sensors (Basel, Switzerland)·2023
Same author

The 2023 wearable photoplethysmography roadmap.

Physiological measurement·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 3, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K

探索卷积神经网络架构用于EEG特征提取

Ildar Rakhmatulin1, Minh-Son Dao2, Amir Nassibi1

  • 1Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括
此摘要是机器生成的。

这篇论文详细介绍了用于脑电图 (EEG) 信号特征提取的卷积神经网络 (CNN) 的创建. 它探索信号处理,数据准备,并评估CNN架构以获得最佳性能.

关键词:
在美国,CNN是CNN.这是一个EEGEEGEEGEEGEEGEEGEEG.机器学习是机器学习.信号处理 信号处理 信号处理

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.0K

相关实验视频

Last Updated: Jul 3, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.6K
Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.0K

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
  • 信号处理 信号处理

背景情况:

  • 脑电图 (EEG) 信号提供了对大脑活动的洞察力,但需要复杂的分析.
  • 从EEG中提取特征对于各种应用至关重要,包括诊断和脑计算机接口.
  • 卷积神经网络 (CNN) 在分析像EEG这样的复杂生物信号方面表现出了前途.

研究的目的:

  • 为开发和微调用于EEG特征提取的CNN提供全面指南.
  • 探索为EEG数据量身定制的基本信号处理和数据准备技术.
  • 分析和分类用于EEG分析的常见CNN架构.

主要方法:

  • 研究了EEG信号特征及其对特征提取的影响.
  • 应用各种信号处理技术:降噪,过,编码,解码和尺寸缩小.
  • 分析并将CNN架构分类为标准,反复卷积,解码器和组合类型.
  • 基于准确度指标和超参数调整的评估架构.

主要成果:

  • 确定了对有效的EEG特征提取至关重要的关键信号处理步骤.
  • 证明了不同CNN架构对于特定EEG分析任务的实用性.
  • 提供了基于经验数据的CNN业绩的比较评估.
  • 编制了常用的CNN架构及其参数的详细附件.

结论:

  • 电脑脑脑电图 (CNN) 是有效的工具,可以从EEG信号中提取有意义的特征.
  • 仔细考虑信号处理和架构选择对于成功的EEG分析至关重要.
  • 这项工作为EEG信号处理领域的研究人员和从业人员提供了宝贵的资源.