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

Prefrontal Speaker-Listener Neural Coupling Supports Speech-in-Noise Comprehension in Normal-Hearing Older Adults: An fNIRS Study.

The European journal of neuroscience·2026
Same author

"Awe-scillations": EEG spectral and complexity representations of awe.

bioRxiv : the preprint server for biology·2025
Same author

Utilizing Tympanic Membrane Temperature for Earphone-Based Emotion Recognition.

Sensors (Basel, Switzerland)·2025
Same author

A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding.

Scientific data·2025
Same author

Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts.

Sensors (Basel, Switzerland)·2025
Same author

Depression Recognition Using Daily Wearable-Derived Physiological Data.

Sensors (Basel, Switzerland)·2025

相关实验视频

Updated: Jul 12, 2025

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.2K

一个大型细粒度的情感计算EEG数据集

Jingjing Chen1,2, Xiaobin Wang1,2, Chen Huang1,2

  • 1Dept. of Psychology, School of Social Sciences, Tsinghua University, Beijing, China.

Scientific data
|October 25, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了FACED数据集,其中包括来自123名情感计算受试者的脑电图 (EEG) 数据. 它使个人能够进行强大的情绪识别,推进人机交互应用.

更多相关视频

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG
12:48

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

17.9K
Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention
05:36

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

7.6K

相关实验视频

Last Updated: Jul 12, 2025

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.2K
Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG
12:48

Investigating Social Cognition in Infants and Adults Using Dense Array Electroencephalography dEEG

Published on: June 27, 2011

17.9K
Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention
05:36

Central and Divided Visual Field Presentation of Emotional Images to Measure Hemispheric Differences in Motivated Attention

Published on: November 16, 2017

7.6K

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
  • 心理学 心理学 心理学

背景情况:

  • 情感计算使用脑电图 (EEG) 来客观地测量情绪.
  • 现有的EEG数据集往往缺乏足够的关于积极情绪的数据,样本大小小小,阻碍了跨主题分析.
  • 需要全面的EEG数据集来平衡积极和消极情绪,并支持强大的跨主题情感计算.

研究的目的:

  • 引入细粒度情感计算EEG数据集 (FACED),以解决现有的情感相关EEG数据集的局限性.
  • 为积极和消极情绪提供一个大规模,细粒度和平衡的数据集.
  • 促进使用EEG信号进行跨主题情感计算的研究.

主要方法:

  • 从123名受试者中记录了32个频道的EEG信号.
  • 利用了28个引发情感的视频片段,分为9种不同的情感类型 (娱乐,灵感,快乐,温柔,愤怒,恐惧,厌恶,悲伤,中立).
  • 通过积极和消极的价值来确保细粒度和平衡的情感分类.

主要成果:

  • 从EEG信号中表现出有效的情绪类别识别,无论是在主体内还是跨主体层面.
  • 验证了FACED数据集对训练和测试情感计算模型的有用性.
  • 展示了使用平衡和多样化的数据集准确分类情绪的潜力.

结论:

  • FACED数据集为推进基于EEG的情感计算提供了宝贵的资源.
  • 数据集支持在不同个体中应用的更具概括性的情绪识别算法的开发.
  • 预计FACED将对情感计算的现实应用做出重大贡献,特别是在人机交互方面.