Edge-Computing Artificial Intelligence for Real-Time Multimodal Biosignal Processing in Medical Devices: A Rigorous Conceptual Framework Integrating Advanced Dynamical Modeling, Bayesian Uncertainty Quantification, Comprehensive Sensitivity Analyses with Sobol Indices and Van der Pol Enhancements, Therapeutic Brain-Computer Interfaces, Hardware Specifications, Real-World Validation, and a Visionary Roadmap

The transformative integration of electronic medical devices—encompassing implantables, wearables, and remote monitoring systems—has enabled the seamless acquisition of multimodal and multigranular biosignals via non-invasive or invasive modalities (e.g., ECG, PPG, EEG, IMU). These intricate datasets encapsulate profound physiological dynamics, poised for sophisticated artificial intelligence exploitation. Mission-critical applications mandate edge-computing AI paradigms to facilitate instantaneous processing, autonomous decision-making, and adaptive feedback in unsupervised settings, yielding interpretable and actionable insights with exceptional precision and reliability. Fueled by embedded technological advancements, including augmented computational capacity and pervasive connectivity through Bluetooth, Wi-Fi, and 5G, alongside miniaturized sensors, this evolution is synergized with algorithmic breakthroughs in TinyML, deep learning, and federated learning, engendering advanced real-time systems. This meticulously crafted conceptual framework elucidates pivotal progress in biomedical engineering, assistive technologies, elderly monitoring, mobile-health (mHealth), smart-health, and therapeutic brain-computer interfaces (BCIs). It harmoniously weaves advanced dynamical systems modeling—augmented with Van der Pol oscillators—Bayesian uncertainty quantification, local and global sensitivity analyses incorporating Sobol variance decomposition, reproducible simulations, comparative evaluations, hardware constraints, real-world validation using public datasets, enhanced federated learning protocols, and a strategic phased roadmap, fostering a cohesive, insightful, and scientifically fortified paradigm \citep{rocha2024edge, wagan2023iomt, abadade2023tinyml, wang2023privacy, liu2023efficacy, altaheri2023deep, dutta2025hybrid, chen2024bci}.