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使用EEG信号和面部分析与专家验证的EmoTrans基于注意力的情绪识别

Ch Anwar Ul Hassan¹, Muhammad Ehatisham-Ul-Haq², Fiza Murtaza²

¹Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan. anwarchaudary@gmail.com.

Scientific reports

|July 2, 2025

概括

这项研究引入了EmoTrans模型,用于使用电脑电图 (EEG) 信号和面部分析进行情绪识别. 在分类情绪状态方面,EmoTrans实现了高精度,为情感计算提供了更生态有效的方法.

科学领域:

情感计算是一种情感计算.
人与计算机的互动.
神经科学是一个神经科学.

背景情况:

由于受控刺激,传统的情感识别缺乏生态有效性.
了解人类情绪对于推进人机交互至关重要.
生理信号和面部表情是情绪状态的关键指标.

研究的目的:

提出并验证EmoTrans模型用于准确的情绪识别.
整合脑电图 (EEG) 和面部视频数据,进行全面的情绪状态分析.
为了提高情绪识别模型的生态有效性.

主要方法:

利用了DEAP数据集,包括参与者观看电影片段的EEG录音和面部视频.
来自EEG信号 (时间,频率,波段域) 和面部视频数据的集成特征.
采用基于注意力的架构来优先考虑功能,并通过机器学习,深度学习和Leave-one-subject-out交叉验证 (LOSO-CV) 进行验证.

主要成果:

EmoTrans实现了很高的准确性:89.3% (兴奋),87.8% (价值),88.9% (主导) 和89.1% (喜欢).
对于幸福,兴奋,平静和痛苦等各种情绪,表现出89%的整体分类准确度.
统计学意义证实,EmoTrans通过对联t测试优于基线模型.

结论:

EmoTrans模型为情感识别提供了一个强大的和生态有效的方法.
整合多模式数据 (EEG和面部分析) 显著提高了情绪分类的准确性.
基于注意力的架构有效地利用相关特征,以便对人类情绪状态有细微的理解.

关键词:

电脑电磁波信号电脑电磁波信号情绪识别情绪识别面部识别功能面部识别功能面部修复视频面部修复视频心理数据心理数据

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