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

Overview

1. Department of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary
2. Department of Integrative Biology, Vellore Institute of Technology, Vellore 632014, India
3. Cognitive Neuroimaging Centre, Experimental Medicine, Nanyang Technological University, Singapore 636921, Singapore
4. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 636921, Singapore
5. Faculty of Medicine, PSG Institute of Medical Sciences & Research, Peelamedu, Coimbatore 641004, India
6. Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden

Abstract

BCI biosensors enable continuous monitoring of neural activity, but existing systems face challenges in scalability, latency, and reliable integration with cloud infrastructure. This work presents a cloud-aware, real-time cognitive grid architecture for multimodal BCI biosensors, validated at the system level through a full physical prototype. The system integrates the BioAmp EXG Pill for signal acquisition with an RP2040 microcontroller for local preprocessing using edge-resident TinyML deployment for on-device feature/inference feasibility coupled with environmental context sensors to augment signal context for downstream analytics talking to the external world via Wi-Fi/4G connectivity. A tiered data pipeline was implemented: SD card buffering for raw signals, Redis for near-real-time streaming, PostgreSQL for structured analytics, and AWS S3 with Glacier for long-term archival. End-to-end validation demonstrated consistent edge-level inference with bounded latency, while cloud-assisted telemetry and analytics exhibited variable transmission and processing delays consistent with cellular connectivity and serverless execution characteristics; packet loss remained below 5%. Visualization was achieved through Python 3.10 using Matplotlib GUI, Grafana 10.2.3 dashboards, and on-device LCD displays. Hybrid deployment strategies—local development, simulated cloud testing, and limited cloud usage for benchmark capture—enabled cost-efficient validation while preserving architectural fidelity and latency observability. The results establish a scalable, modular, and energy-efficient biosensor framework, providing a foundation for advanced analytics and translational BCI applications to be explored in subsequent work, with explicit consideration of both edge-resident TinyML inference and cloud-based machine learning workflows.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• physionet.org/content/eegmmidb, at PhysioNet; found in the references

Data Availability Statement

The data that support the findings of this study are not publicly available as the evaluation dataset comprises proprietary firmware configurations, author-managed cloud infrastructure parameters, and pseudonymized session telemetry logs generated during controlled bench evaluation; access is restricted to protect confidential and proprietary information. These restrictions are of a technical and operational nature and are unrelated to human-subject privacy considerations. The data may be made available upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 12 keywords, 5 MeSH terms, 1 funder, 95 references.

Cite

This paper

Ghosh, S., Bhuvanakantham, R., Sindhujaa, P., Harishita, P. B., Mohan, A., Gulyás, B., Máthé, D., & Padmanabhan, P. (2026). A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System. Biosensors, 16(3), 157. https://doi.org/10.3390/bios16030157

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/bios16070394

Hybrid Edge-Cloud Asymmetric Analytics for Portable Multimodal BCI Biosensors.

Journal: Biosensors

In common: physionet.org/content/eegmmidb, methods / tools, EEG, 6 references, author Domokos Máthé

[2] doi:10.1038/s41598-026-56070-y [code]

SSDLabeler: realistic semi-synthetic data generation for multi-label artifact classification in EEG.

Journal: Scientific reports

In common: physionet.org/content/eegmmidb, EEG, 4 references

[3] doi:10.3390/s26134045

Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.

Journal: Sensors (Basel, Switzerland)

In common: physionet.org/content/eegmmidb, methods / tools, EEG, 2 references

[4] doi:10.1371/journal.pcbi.1014112 [code]

Opposing cortical forces: Alpha slowing and sensorimotor mu acceleration during motor-related BCI training.

Journal: PLoS computational biology

In common: physionet.org/content/eegmmidb, EEG, 2 references

[5] doi:10.1371/journal.pone.0354976 [code]

Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture.

Journal: PloS one

In common: methods / tools, EEG, 3 references

[6] doi:10.3390/s26134019 [code]

NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials.

Journal: Sensors (Basel, Switzerland)

In common: physionet.org/content/eegmmidb, EEG, 1 reference

[7] doi:10.3390/brainsci16080873

Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.

Journal: Brain sciences

In common: physionet.org/content/eegmmidb, methods / tools, EEG

[8] doi:10.3390/s26113410

An Arduino-Based, Portable Prototype for the Recording and Analysis of EEG Signals to Support Self-Detection and Self-Monitoring of Stress.

Journal: Sensors (Basel, Switzerland)

In common: physionet.org/content/eegmmidb, methods / tools, EEG

[9] doi:10.1093/bioinformatics/btag169 [code]

Bidirectional Mamba-2 boosts EEG super-resolution via regression and diffusion.

Journal: Bioinformatics (Oxford, England)

In common: physionet.org/content/eegmmidb, methods / tools, EEG

[10] doi:10.3389/fnhum.2026.1781338 [code]

Golden ratio organization in human EEG is associated with theta-alpha frequency convergence: a multi-dataset validation study.

Journal: Frontiers in human neuroscience

In common: physionet.org/content/eegmmidb, methods / tools, EEG

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.