意识到异质性的多模体生理信号融合策略,基于情感识别的结合对比学习
概括
这项研究引入了一种新的跨模式对比学习策略,通过解决信号异质性来改善多模式情绪识别. 拟议的方法通过有效地融合各种生理信号来提高性能.
科学领域:
- 情感计算是一种情感计算.
- 机器学习是机器学习.
- 生物医学信号处理
背景情况:
- 生理信号表现出非静止性和跨主体的变性,导致跨模式,通道和时间的异质性.
- 这种异质性显著影响了多模式生理信号融合用于情绪识别的有效性.
- 现有的方法很难同时解决这些信号变化的多元来源.
研究的目的:
- 提出一种综合的跨模式对比学习策略,以减轻信号异质性.
- 通过改进信号融合来提高多式联动情绪识别的性能.
- 为了解决来自非静止信号的变化和跨主体差异.
主要方法:
- 引入了基于图形注意网络 (GAT) 的可学习视图增强来模拟信号变化.
- 在当前和之前的时间补丁之间实施时间对比学习,以减少时间异质性.
- 在视图内和视图之间应用跨道对比学习,以减少跨模式和跨道异质性.
主要成果:
- 拟议的模型超越了DEAP,DREAMER和PhyMER数据集上的最先进的多式联络融合模型.
- 可学习的视图增强,时间对比学习和空间对比学习显著促进了性能提升.
- 该模型有效地利用了情感表现的不同模式的互补信息.
结论:
- 拟议的跨模式对比学习策略有效地减轻了多模式生理数据中的信号异质性.
- 与现有方法相比,该方法在多式联络情绪识别方面表现优越.
- 该策略成功地利用了模式间的互补性,以实现强大的情绪状态表现.
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