<b>Dataset of Distributed EEG-based Internet of Medical Things (IoMT) Architectures for Remote Brain Monitoring and Neurorehabilitation: An Integrative Review (2019–2024)</b>

This dataset compiles structured variables extracted from peer-reviewed studies included in an integrative literature review on distributed electroencephalography (EEG)-based Internet of Medical Things (IoMT) architectures for remote brain monitoring and neurorehabilitation. The review followed systematic search procedures across major scientific databases and applied PRISMA-based screening to identify relevant studies published between 2019 and 2024. The extracted variables cover multiple dimensions of EEG–IoMT systems, including clinical context, EEG acquisition devices, signal acquisition and preprocessing methods, feature extraction strategies, communication protocols, distributed architectures, processing locations (edge, hybrid, or cloud), security mechanisms, and machine learning paradigms, as well as reported performance metrics. To address heterogeneity across studies, a standardized taxonomy for signal processing methods and reporting status was applied during data extraction and structuring. The resulting dataset enables reproducible research, comparative analyses, and benchmarking of EEG–IoMT systems, supporting meta-research on distributed neurotechnology and informing the development of scalable, secure, and clinically applicable neurorehabilitation solutions. This dataset was developed at the Federal University of Health Sciences of Porto Alegre (UFCSPA), Brazil, as part of a doctoral research project in Rehabilitation Sciences. Instructions: This dataset is intended for research and educational use, particularly for comparative analyses of distributed EEG–IoMT architectures in remote brain monitoring and neurorehabilitation. Users may apply statistical, signal processing, or machine learning approaches to investigate relationships between architectural design choices, signal processing pipelines, and reported performance metrics. As the dataset is derived from published literature, it does not include raw EEG signals. Instead, it provides structured, study-level variables extracted from reported methodologies and results. Users should account for heterogeneity across studies, including differences in experimental design, reporting practices, and data completeness. Missing values and non-reported variables should be handled appropriately when conducting analyses or benchmarking tasks. Dataset columns: study_title study_publication_year study_journal_scope study_author_affiliation_countries clinical_condition_group clinical_neurofunctional_domain clinical_application_context clinical_neurorehabilitation_applicability clinical_health_technology_adequacy eeg_system eeg_channel_density temporal_spatial_preprocessing_methods data_preparation_and_structuring acquisition_level_signal_conditioning signal_analysis_and_transformation_methods extracted_feature_domains arch_communication_protocols arch_architecture_type arch_processing_location arch_security_mechanisms model_classifier_paradigm perf_accuracy perf_precision perf_recall perf_f1_score perf_specificity perf_latency_ms