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<p>In this work, we propose a fog-enabled Electroencephalogram (EEG) architecture using the Internet of Things (IoT) for parallel and distributed healthcare data computing. EEG plays a vital role in a Brain-Computer Interface (BCI) systems for recording the electrical activities of the human brain from the scalp. On the other hand, the integration of IoT networks with EEG systems enables efficient computing and services. In the conventional healthcare systems, a typical IoT-based healthcare system uses the cloud computing paradigm to manage time-critical healthcare data; moreover, the fog-assisted EEG systems are for single EEG applications. However, the use of a fog computing paradigm for a single EEG system is not an efficient solution in terms of Internet computing resource management. Therefore, in this work, we introduce a Fog-enabled EEG architecture (F2E) with an aim to serve time-critical data acquired from the EEG devices in a healthcare IoT network. In the proposed architecture, multiple fog devices collaboratively process the EEG data in a single integrated IoT platform. As the proposed architecture for EEG applications is new, we focus on developing the mathematical model of the F2E architecture and discuss the crucial aspects of the same. Additionally, we devise a dynamic optimal Fog Head (FH) selection within the computing network using a weighted multi-criteria decision-making approach. From the simulation, we observe that the average propagation delay is reduced by approximately <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(95 \%\)</EquationSource> </InlineEquation> using fog computing as compared to the cloud. Further, the proposed technique reduces the total delay by <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(83.87 \%\)</EquationSource> </InlineEquation> compared to the existing technique, showing the effectiveness of this work.</p>

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F2E: fog-enabled EEG architecture for healthcare services and computing

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Rama Krushna Rath,
  • Sreeja SR,
  • Arijit Roy

摘要

In this work, we propose a fog-enabled Electroencephalogram (EEG) architecture using the Internet of Things (IoT) for parallel and distributed healthcare data computing. EEG plays a vital role in a Brain-Computer Interface (BCI) systems for recording the electrical activities of the human brain from the scalp. On the other hand, the integration of IoT networks with EEG systems enables efficient computing and services. In the conventional healthcare systems, a typical IoT-based healthcare system uses the cloud computing paradigm to manage time-critical healthcare data; moreover, the fog-assisted EEG systems are for single EEG applications. However, the use of a fog computing paradigm for a single EEG system is not an efficient solution in terms of Internet computing resource management. Therefore, in this work, we introduce a Fog-enabled EEG architecture (F2E) with an aim to serve time-critical data acquired from the EEG devices in a healthcare IoT network. In the proposed architecture, multiple fog devices collaboratively process the EEG data in a single integrated IoT platform. As the proposed architecture for EEG applications is new, we focus on developing the mathematical model of the F2E architecture and discuss the crucial aspects of the same. Additionally, we devise a dynamic optimal Fog Head (FH) selection within the computing network using a weighted multi-criteria decision-making approach. From the simulation, we observe that the average propagation delay is reduced by approximately \(95 \%\) using fog computing as compared to the cloud. Further, the proposed technique reduces the total delay by \(83.87 \%\) compared to the existing technique, showing the effectiveness of this work.