Multimodal Neural Signal Modeling for Personalized Neurorehabilitation Using Brain–Computer Interfaces and Reinforcement Learning

Abstract

Personalized neurorehabilitation increasingly depends on computational systems that can interpret neural activity, infer evolving patient states, and adapt therapeutic assistance over time. Brain-computer interfaces provide a direct pathway for translating endogenous neural signals into control actions, while reinforcement learning offers a framework for sequential decision-making under uncertainty. When these approaches are combined with multimodal neural and behavioral modeling, they create opportunities for rehabilitation systems that are more responsive, patient-specific, and functionally meaningful than fixed protocol-driven devices. However, the integration of such systems raises substantial architectural, clinical, ethical, and infrastructural challenges. This paper examines multimodal neural signal modeling for personalized neurorehabilitation using brain-computer interfaces and reinforcement learning from a system-level perspective. It analyzes the structural trade-offs among signal fidelity, latency, autonomy, safety, and interpretability. It considers how reinforcement learning can support adaptive rehabilitation while remaining constrained by clinical governance, human oversight, and fairness requirements. The discussion further addresses deployment across hospitals, homes, and community settings, including issues of data infrastructure, maintenance, energy use, and long-term sustainability. It argues that personalized neurorehabilitation should be understood not merely as a decoding problem but as a socio-technical system requiring robust uncertainty management, transparent reward design, inclusive data practices, and policy frameworks that protect neural privacy and patient agency. The conclusion emphasizes that future progress depends on integrating signal processing, control theory, clinical expertise, and governance into a coherent, patient-centered architecture.