使用轻量级深度学习模型的情感识别嵌入式系统
Mehdi Bazargani1, Amir Tahmasebi1, Mohammadreza Yazdchi1
1Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Journal of medical signals and sensors
|October 9, 2023
概括
这项研究使用脑电图 (EEG) 信号和卷积神经网络 (CNN) 模型准确诊断情绪状态. 开发的轻量级实时系统可以实现高准确度,用于实际的人机交互应用.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人与计算机的交互
背景情况:
- 情绪状态诊断对于增强人机交互 (HCI) 至关重要.
- 电脑电图 (EEG) 信号为情绪识别提供准确和有信息的数据.
- 以前的研究表明EEG信号与情绪状态之间存在关联.
研究的目的:
- 开发和评估使用EEG信号进行情绪诊断的卷积神经网络 (CNN) 模型.
- 创建一个轻量级和可实现的模型,用于实时情绪识别在嵌入式系统上,如树Pi.
- 在主体依赖和主体独立的设置中评估模型性能.
主要方法:
- 在EEG信号处理中应用了三种CNN模型 (EEGNet,ShallowConvNet,DeepConvNet).
- 使用基线删除预处理来提高分类准确性.
- 在DEAP数据集上以主体依赖和主体独立的配置评估模型.
- 优化了CNN模型以在Raspberry Pi上轻量化实现,以实时识别情绪.
主要成果:
- 获得了高分类准确度:在取决于主体的环境中,对价值的99.10%和兴奋的99.20%.
- 在独立于主体的设置中,获得了对价值的90.76%准确性和对唤起的90.94%准确性.
- 在嵌入式系统上展示了实时情绪识别能力.
结论:
- 该研究成功开发了一种高度准确和可实施的基于EEG的情绪诊断模型.
- 轻量级的CNN模型适用于人机交互中的实用实时应用.
- 结果与相关工作相比较有利,表明该领域取得了重大进展.
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