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一个跨背景情绪EEG数据集用于跨背景情绪解码

Xin Xu¹, Xinke Shen², Xuyang Chen¹

¹Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.

Scientific data

|July 3, 2025

概括

这项研究引入了多背景情绪EEG (EmoEEG-MC) 数据集,使得在不同背景下能够更好地解读情绪. 这些发现显示出有希望的跨背景情绪识别,推动了情感计算的发展.

科学领域:

神经科学是一个神经科学.
情感计算是一种情感计算.
心理学心理学心理学

背景情况:

基于EEG的情绪解码对于理解情绪和在心理健康和人机交互方面开发应用至关重要.
目前的数据集缺乏多上下文数据,限制了情感解码模型的概括性.
情感解码在各种诱导环境中进行概括的能力在很大程度上是未被探索的.

研究的目的:

介绍多语境情感EEG (EmoEEG-MC) 数据集,这是研究不同语境情感解码的新资源.
通过使用EEG和外围生理数据,研究跨背景情绪解码的可行性.
为推进情感计算和理解情感的神经基础提供基础.

主要方法:

收集了来自60名参与者的64通道EEG和外周生理数据,在两个情感诱导环境下:视频诱导和图像诱导.
唤起了七种不同的情绪类别:快乐,灵感,温柔,恐惧,厌恶,悲伤和中立.
使用具有L1规范化的支持向量机来进行跨上下文情感解码分析.

主要成果:

在二元分类 (正面与负面情绪) 中获得了66.7%的准确性,在七类情绪分类中获得了28.9%的准确性,两者都明显高于偶然.
演示了情绪解码模型在不同诱导环境中概括的潜力.
通过主观报告验证了情感经历.

结论:

EmoEEG-MC数据集是推动情绪解码和情感计算研究的宝贵资源.
跨语境情感解码是可行的,并显示出对现实世界的应用的潜力.
这项工作有助于更深入地了解各种背景下情绪的神经基质.

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