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A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System.

Sayantan Ghosh1,2, Raghavan Bhuvanakantham3,4, Padmanabhan Sindhujaa5

  • 1Department of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary.

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Summary
This summary is machine-generated.

This study introduces a cloud-aware architecture for Brain-Computer Interface (BCI) biosensors, enabling real-time monitoring and edge AI processing. The system demonstrates scalable, energy-efficient data handling for advanced BCI applications.

Keywords:
EEGEMGMultimodal Brain–Computer Interfacebiosensorscloud analyticscontinuous biosignal telemetrydata processingedge computingelectrophysiological signalsreal-time signal analysisremote health monitoringscalable data storage

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Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Existing Brain-Computer Interface (BCI) biosensors struggle with scalability, latency, and cloud integration.
  • Continuous neural activity monitoring requires robust and efficient data processing frameworks.

Purpose of the Study:

  • To develop and validate a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors.
  • To integrate edge-resident TinyML (Tiny Machine Learning) with cloud infrastructure for enhanced BCI data analytics.

Main Methods:

  • A physical prototype integrating BioAmp EXG Pill, RP2040 microcontroller, and environmental sensors was developed.
  • A tiered data pipeline (SD card, Redis, PostgreSQL, AWS S3) managed data flow from acquisition to archival.
  • Edge-resident TinyML enabled on-device feature extraction and inference, communicating via Wi-Fi/4G.

Main Results:

  • The system achieved consistent edge-level inference with bounded latency and packet loss below 5%.
  • Cloud-assisted analytics showed variable delays typical of cellular networks and serverless computing.
  • Hybrid deployment strategies facilitated cost-efficient validation while maintaining architectural integrity.

Conclusions:

  • The developed framework is scalable, modular, and energy-efficient, supporting advanced analytics for translational BCI applications.
  • The architecture effectively combines edge-resident TinyML inference with cloud-based machine learning workflows.
  • This work lays the foundation for future BCI research and development with improved data handling capabilities.