雾计算用于控制工业中的网络物理系统,使用BCI
Paula Ivone Rodríguez-Azar1, Jose Manuel Mejía-Muñoz2, Oliverio Cruz-Mejía3
1Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究介绍了使用脑电图 (EEG) 信号的脑电脑接口 (BCI) 的雾计算解决方案. 与控制网络物理系统的云计算方法相比,雾计算显著降低了延迟时间.
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
- 工程 工程师 工程师 工程师
背景情况:
- 大脑计算机接口 (BCI) 利用脑信号,如EEG用于控制应用.
- 云计算在EEG数据量方面面临挑战,导致工业BCI系统中的延迟和带宽问题.
研究的目的:
- 在工业BCI系统中引入和评估用于处理EEG信号的雾计算解决方案.
- 为了减少BCI控制的网络物理系统的延迟和提高响应时间.
主要方法:
- 实现了雾计算架构,使用机器学习处理EEG信号的雾节点.
- 使用深度学习编码器从EEG信号中提取特征.
- 使用随机森林分类器进行命令识别,并将其性能与其他分类器进行比较.
主要成果:
- 随机森林分类器在将EEG信号分类为命令方面表现出卓越的性能.
- 雾计算模拟器比较显示,与纯粹基于云的方法相比,延迟时间减少.
- 拟议的雾计算解决方案有效地将EEG信号转换为网络物理系统控制的命令.
结论:
- 雾计算提供了一个可行的解决方案,以减轻工业BCI应用中的延迟和带宽限制.
- 开发的机器学习模块,特别是随机森林分类器,有效地处理EEG数据用于BCI控制.
- 这种方法增强了由BCI技术驱动的网络物理系统的实时控制能力.
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