一个域泛化和残余网络基于生理信号的情绪识别
Junnan Li1,2,3, Jiang Li1,2,3,4, Xiaoping Wang1,2,3
1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Cyborg and bionic systems (Washington, D.C.)
|May 20, 2025
概括
这项研究引入了一种新的方法,用于从生理信号 (ERPS) 识别情绪. 该方法有效地捕获信号相关性,并减轻时间共变量转移以提高准确性.
科学领域:
- 物理计算的物理计算.
- 情感计算是一种情感计算.
- 机器学习用于信号处理.
背景情况:
- 从生理信号 (ERPS) 识别情绪是具有挑战性的,因为非静止,高频数据.
- 关键的挑战包括捕捉信号间的相关性和解决时间共变量转移 (TCS).
研究的目的:
- 为ERPS (DGR-ERPS) 提出一个域泛化和基于残余网络的方法.
- 解决现有ERPS方法在特征提取和时间共变量转移方面的局限性.
主要方法:
- 预先提取的时间和频率域特征被组合成新的时间序列.
- 时间序列数据被转换为3D图像,输入到基于残余的特征编码器 (RBFE).
- 使用域泛化技术来减轻时间共变量转移 (TCS).
主要成果:
- 拟议的DGR-ERPS方法在情绪识别方面表现出卓越的表现.
- 在时间共变量转移 (TCS) 和非TCS情景中验证了有效性.
- 在两个真实世界的数据集上进行了广泛的实验,证实了该方法的稳定性.
结论:
- DGR-ERPS方法有效地处理生理信号中的相关性和时间共变量转移.
- 这种方法为准确而强大的情绪识别提供了显著的进步.
- 这些发现对使用ERPS的应用有着广泛的影响.
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