关节低级张量融合和跨模态注意力用于基于多模态生理信号的情绪识别
Xin Wan1, Yongxiong Wang1, Zhe Wang1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.
Physiological measurement
|June 25, 2024
概括
这项研究引入了一种使用生理信号的新型多式联络式情绪识别方法. 该方法有效地融合了脑电图,眼电图,肌电图和皮反应数据,以提高准确性.
科学领域:
- 人与计算机的交互
- 情感计算是一种情感计算.
- 生物医学信号处理
背景情况:
- 使用生理信号识别情绪对于推进人机交互至关重要.
- 以前的研究通常依赖于单模数据,限制了对复杂情感相互作用的理解.
- 整合多模式生理数据在有效利用补充信息方面提出了挑战.
研究的目的:
- 为增强多式联络情绪识别提出一个中间融合策略.
- 通过整合脑电图,眼电图,肌电图和皮反应数据来提高情绪识别的稳定性.
- 为了更准确地分类情绪,探索不同生理模式之间的相互作用.
主要方法:
- 一种新的中间融合策略,结合了低级张量融合和跨模态关注.
- 从手工制作的电脑图,电眼图,电肌图和皮反应的特征中提取个体特征.
- 低级张量融合以捕捉模态相互作用,随后进行跨模态关注以重新校准特征重量.
主要成果:
- 在DEAP数据集上进行的独立实验验证实了拟议的方法.
- 在价值分类方面达到73.82%的准确度,在兴奋分类方面达到74.55%.
- 证明了融合策略在增强多式联络情绪识别方面的有效性.
结论:
- 拟议的中间融合方法通过有效地整合多模式生理信号,显著提高了情感识别准确度.
- 低级张量融合和跨模态注意力的结合为捕捉跨模态关系提供了一个强大的方法.
- 这项研究为情感计算领域做出了贡献,为基于生理学的情感识别提供了更有效的方法.
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