EAV:用于在对话环境中识别情绪的EEG音频视频数据集
Min-Ho Lee1, Adai Shomanov1, Balgyn Begim1
1Nazarbayev University, Department of Computer Science, Astana, 010000, Republic of Kazakhstan.
Scientific data
|September 19, 2024
概括
这项研究引入了42名参与者的第一个公共多式情绪数据集 (EEG,音频,视频),使人工智能模型能够更好地理解对话中的人类情绪.
科学领域:
- 神经科学和机器学习
- 人与计算机的交互
- 情感计算是一种情感计算.
背景情况:
- 了解人类情绪对于开发先进的人机界面至关重要.
- 人类的行为和心理生理反应受到社会互动和感知输入的影响.
- 了解大脑功能和行为可以加速人工智能的发展,使其具有类似人类的能力.
研究的目的:
- 为研究引入一种新的多式联络情绪数据集.
- 促进能够识别人类情绪的人工智能模型的开发.
- 从神经科学和机器学习的角度,推进对人类情感过程的理解.
主要方法:
- 收集了多式联络数据,包括30通道脑电图 (EEG),42名参与者的音频和视频录制.
- 参与者进行基于提示的对话,引起五种情绪:中立,愤怒,幸福,悲伤和平静.
- 用深度神经网络 (DNN) 方法对每个模式的基线情感识别性能进行评估.
主要成果:
- 这项研究创建了EEG-Audio-Visual (EAV) 数据集中的情绪,这是同类的第一个公共资源.
- 数据集包括42名参与者的8,400次互动,涵盖听力和说话角色.
- 使用DNN的基线性能评估建立了跨模式情绪识别的初始基准.
结论:
- 电子情绪识别数据集对情绪识别研究做出了重大贡献,特别是在对话环境中.
- 预计这项资源将提高人工智能模型理解和复制人类情绪状态的能力.
- 该数据集支持跨学科研究,将基础神经科学与应用机器学习相结合.
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