Distributed EEG-IoMT Architectures for Remote Brain Monitoring, 2019–2024

A 2019–2024 review dataset compiling structured variables from peer-reviewed studies on distributed electroencephalography-based Internet of Medical Things architectures for remote brain monitoring and neurorehabilitation. The dataset, 59.5 KB in size, was developed at the Federal University of Health Sciences of Porto Alegre (UFCSPA), Brazil, as part of doctoral research. It covers clinical context, EEG devices, signal processing, communication protocols, architectures, security, and machine learning paradigms.

Use Cases

• Comparative analysis of architectural design choices based on reported communication protocols and processing locations.
• Benchmarking machine learning paradigm performance based on reported metrics across different clinical contexts.
• Investigating relationships between signal preprocessing methods and system performance in neurorehabilitation applications.
• Meta-research on security mechanism adoption in EEG-IoMT systems based on the structured taxonomy.

Strengths

• Data extracted using a standardized taxonomy to address heterogeneity across studies.
• Covers a defined time range of studies published between 2019 and 2024.
• Variables span multiple dimensions including clinical, technical, and architectural aspects.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Dataset is small at 59.5 KB, indicating a limited scope of extracted study metadata.

Provenance

Source

Federal University of Health Sciences of Porto Alegre (UFCSPA), Brazil.

Collection Method

Structured extraction from peer-reviewed studies identified via a systematic, PRISMA-based integrative literature review.

Time Range

2019–2024

Freshness

Last updated 2026-04-03 17:33:47; freshness should be verified.

Geography

Studies likely have global authorship, but specific geographic coverage is not stated.