基于EEG的双阶段情绪识别框架,结合了对比学习和双重注意力编码器.
IEEE journal of biomedical and health informatics
|February 27, 2026
概括
这项研究介绍了CLDAE,这是一种基于电脑电图 (EEG) 的情绪识别的新框架. 通过整合对比学习和双重注意力机制,CLDAE增强了跨主题概括和主题内部个性化.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 基于脑电图 (EEG) 的情绪识别在跨主体概括和主体内部个性化方面面临挑战.
- 现有的模型往往需要大量的个性化数据或努力适应个别的神经特征.
研究的目的:
- 提出一种新的基于EEG的情绪识别框架,CLDAE,解决概括和个性化限制.
- 整合对比式学习和双重注意力特征提取机制,以提高性能.
主要方法:
- CLDAE框架包括两个阶段:对比学习预训练和情绪识别微调.
- 使用数据增强与交叉主体EEG信号进行预训练.
- 采用双重注意力编码器,将时间和通道注意力结合起来用于特征提取.
主要成果:
- 在主体内和跨主体情绪识别任务中,CLDAE实现了竞争性表现.
- 在MAN数据集上证明了高准确性:95.12%用于主体内识别和75.29%用于跨主体识别.
- 在情绪识别准确度方面表现优于基线方法.
结论:
- 拟议的CLDAE框架有效地增强了基于EEG的情绪识别.
- 对比学习和双重注意力机制的整合证明对个性化和泛化都有好处.
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