TSCL-LwF:一个通过多尺度CNN和增量学习策略的跨主体情感识别模型
Chunting Wan1, Xing Tang1, Cong Hu1
1School of Electronic Engineering and Automation, Postdoctoral Mobile Station for Instrument Science and Technology, Guilin University of Electronic Technology, Guilin 541004, China.
Brain sciences
|January 28, 2026
概括
这项研究介绍了TSCL-LwF,这是一种使用稀疏通道电脑图 (EEG) 进行跨主体情绪识别的新型模型. 该模型有效地捕捉了时空特征,从而实现了实际的可穿戴情感计算应用.
科学领域:
- 情感计算是一种情感计算.
- 人与计算机的交互
- 生物医学信号处理
背景情况:
- 可穿戴的情感人机交互需要舒适的,稀疏通道电脑图 (EEG) 信号.
- 有限的EEG信息和主体间的变异性对跨主体情绪识别构成挑战.
研究的目的:
- 开发一个强大的跨主体情绪识别模型,使用稀疏通道EEG.
- 为了解决稀疏数据的局限性和个人在情感识别方面的差异.
主要方法:
- 拟议的TSCL-LwF模型结合了多尺度卷积网络 (TSCL) 和不遗忘的学习 (LwF).
- TSCL从稀疏通道EEG中提取时间空间特征.
- LwF策略适应目标域数据,同时保留源域知识.
主要成果:
- 在DEAP数据集上获得了价值的77.26%准确度和兴奋分类的80.12%.
- 在自主收集的EPPVR数据集上表现出卓越的性能.
结论:
- 使用稀疏通道EEG成功跨主体情绪识别是可行的.
- 这有助于开发用于情绪识别的实用可穿戴EEG技术.
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