IF-MMCL:一个以个人为中心的网络,用于跨主体情感识别的多视图和多模式对比学习
Qiaoli Zhou1,2, Jiawen Song3, Yi Zhao3
1School of Computer, Shenyang Aerospace University, Shenyang, 110136, Liaoning, China. zhou_qiao_li@hotmail.com.
Medical & biological engineering & computing
|August 28, 2025
概括
这项研究介绍了IF-MMCL,一种使用脑电图 (EEG) 和多模式数据的情感识别网络. 该模型增强了有限数据的大脑计算机接口 (BCI) 的概括性.
科学领域:
- 神经科学
- 计算机科学
- 机器学习
背景情况:
- 脑电图 (EEG) 对于大脑与计算机接口 (BCI) 在情感识别方面的研究至关重要.
- 有效的情感识别模型需要从EEG数据中提取多视图特征.
- 挑战包括多特征交互和情感识别模型的域调整.
研究的目的:
- 提出一个创新的网络,IF-MMCL,用于情感识别.
- 通过使用多模式,多视图数据来解决多功能交互和域调整.
- 改进BCI研究中的模型概括性,特别是有限的数据.
主要方法:
- 开发了IF-MMCL,一个以个人为中心的网络,利用多模式数据进行多视图表示.
- 利用以个人为中心的对比学习来增强模型的概括性.
- 采用多视图特征提取和多特征关系计算的多种结构.
主要成果:
- 在四个公共情绪数据集上验证了IF-MMCL,并进行了各种分类任务.
- 与以前的方法相比,在一个人离开的实验中表现出更好的表现.
- 用有限的数据实现了改进的模型通用化.
结论:
- 在基于EEG的情感识别中,IF-MMCL有效处理多特征交互和域调整.
- 拟议的网络显著增强了BCI应用的模型通用化.
- IF-MMCL代表了使用有限的受试者数据进行情感识别的有希望的进步.
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