Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Niraparib promotes ferroptosis by inhibiting TM4SF1 expression through ALKBH1-mediated 6mA modification in BRCA wild-type ovarian cancer.

Frontiers in pharmacology·2026

Same author

BN-Embedded Dibenzoullazine: Synthesis, Structures, and Properties.

Organic letters·2026

Same author

Controlled Synthesis of Cyclopenta-Fused B<sub>2</sub>N<sub>2</sub>-Pyrene and Diazaborepin: Structures and Photophysical Properties.

Organic letters·2026

Same author

Catheter monitoring in intelligent endovascular navigation systems: interactive simulations and mixed reality for enhanced navigational awareness.

International journal of computer assisted radiology and surgery·2026

Same author

High-Spin Pt Sites of Intermetallic Compound via Pinning Effect Boost Oxygen Reduction Performance.

Angewandte Chemie (International ed. in English)·2026

Same author

Cardiac Structural and Functional Evaluation Using a Heart Motion Correction Algorithm for Coronary Computed Tomography Angiography in Patients With High Heart Rates.

Reviews in cardiovascular medicine·2026

Search research articles

相关实验视频

Updated: Jan 10, 2026

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

客观情绪评估使用三重注意网络为基于EEG的大脑计算机接口.

Lihua Zhang^1,2, Xin Zhang^1,2, Xiu Zhang^1,2

¹Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China.

|November 27, 2025

概括

此摘要是机器生成的。

这项研究引入了三重注意网络 (TANet),用于增强脑电图 (EEG) 情绪识别. 通过整合复杂的EEG数据分析的多个注意力机制,TANet显著提高了准确性.

关键词:

注意力机制注意力机制大脑计算机接口深度学习是一种深度学习.电脑脑电图 (EEG) 是一种电脑电图.情绪评估情绪评价情绪评估

更多相关视频

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

相关实验视频

Last Updated: Jan 10, 2026

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

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:

情绪识别对于脑-计算机接口和人-计算机交互至关重要.
电脑电图 (EEG) 是情感计算的关键生理信号,因为它具有时间分辨率和非侵入性.
脑电图信号在情绪识别方面存在挑战,原因是噪音和变异性.

研究的目的:

开发一种用于基于EEG的精确情绪识别的新框架.
解决EEG信号固有的复杂性和噪声问题.
通过使用先进的深度学习技术,提高情绪评估系统的性能.

主要方法:

提出了一种三重注意网络 (TANet),集成了合规器,卷积块注意模块 (CBAM) 和相互交叉模式注意 (MCA).
调整器捕捉时间依赖性,CBAM细化空间特征,MCA融合了差异和功率光谱密度.
在DEAP和SEEDEEG情绪数据集上评估了TANet.

主要成果:

在SEED数据集上,TANet使用特定主题的交叉验证实现了98.51%的准确性.
在DEAP数据集中,TANet在价值方面达到99.69%,在兴奋方面达到99.67%,使用细分级别的分割.
与现有方法相比,表现出优越的性能,突出了注意力机制的互补效应.

结论:

TANet为EEG情绪识别提供了一种高性能,强大的解决方案.
提供了对生理信号处理的多维注意力的理论见解.
为开发基于EEG的先进情绪评估系统提供实用指导.