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

Retraction Note: Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.

Scientific reports·2026
Same author

Quantitative investigation on working memory patterns through EEG based on visual attention task for children with learning disability.

Frontiers in systems neuroscience·2026
Same author

Path informed adaptive trend analyzer using Hilbert Huang transform for electric vehicle driving range prediction.

Scientific reports·2026
Same author

Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.

Scientific reports·2026
Same author

ZuraNet: a hybrid rule-based intrusion detection system with deep learning for securing SCADA-driven cyber-physical systems.

Scientific reports·2026
Same author

Epidemiology, diagnosis and emerging therapies for Lyme disease of the Northern Hemisphere.

International journal of emergency medicine·2026

相关实验视频

Updated: Jun 5, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

基于EEG的智能情绪识别使用元启发式优化和混合深度学习技术.

M Karthiga1, E Suganya2, S Sountharrajan3

  • 1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamilnadu, India.

Scientific reports
|December 5, 2024
PubMed
概括

这项研究引入了一种先进的脑电脑接口 (BCI),用于使用脑电图 (EEG) 数据进行情绪识别. 新的混合CNN-ABC-GWO模型在分类情绪状态方面取得了卓越的准确性.

关键词:
人工蜜蜂殖民地卷积神经网络是一种卷积神经网络.一个电电图,一个电电图.电脑脑电图 (EEG) 是一种电脑电图.灰狼优化器 灰狼优化器混合学习方法 混合学习方法

更多相关视频

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

1.7K
Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

12.3K

相关实验视频

Last Updated: Jun 5, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
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

1.7K
Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex
09:00

Investigating the Function of Deep Cortical and Subcortical Structures Using Stereotactic Electroencephalography: Lessons from the Anterior Cingulate Cortex

Published on: April 15, 2015

12.3K

科学领域:

  • 神经科学和人工智能 人工智能
  • 大脑与计算机接口 (BCI) 应用程序
  • 使用生理信号识别情绪识别.

背景情况:

  • 从脑电图 (EEG) 数据中识别情绪对于被动脑电脑接口 (BCI) 应用至关重要.
  • 对EEG信号的准确分析对于区分情绪状态 (正,中性,负) 是必不可少的.
  • 现有的方法需要改进,以提高情绪识别性能.

研究的目的:

  • 开发一个强大的系统来分析EEG数据来分类人类的情绪状态.
  • 通过去除人工物和优化特征提取来提高EEG信号质量.
  • 为了评估一种新的混合模型的表现,用于情感识别.

主要方法:

  • 脑电图数据的预处理包括独立组件分析 (ICA) 进行人工物移除 (电肌图 - EMG,电眼图 - EOG).
  • 信号过将EEG细分为α,β,gamma和theta频段. 信号过将EEG细分为α,β,gamma和theta频段. 信号过将EEG细分为α,β,gamma和theta频段.
  • 特征提取使用混合的元启发式优化技术 (人工蜂群 - ABC和灰狼优化器 - GWO),然后进行卷积神经网络 (CNN) 分类与超参数调整.

主要成果:

  • 混合CNN-ABC-GWO模型在SEED和DEAP数据集上实现了大约99%的准确性.
  • 与单一技术和其他混合学习方法相比,拟议的模型表现出卓越的性能.
  • 该系统在DEAP数据集上实现了100%的准确性,表明在情感识别方面具有很高的效率.

结论:

  • 开发的混合CNN-ABC-GWO模型显著提高了从EEG数据中识别情绪的准确性.
  • 这种方法为需要可靠的情绪状态识别的被动BCI应用提供了有希望的进步.
  • 该方法为分析与人类情绪相关的复杂EEG模式提供了强大的工具.