Current brain computer interface (BCI) systems assume that neural signals can be decoded through relatively stable mappings between electrophysiological activity and computational outputs. However, empirical evidence in computational neuroscience and neural signal processing indicates that brain activity exhibits strong non-stationarity driven by plasticity, contextual modulation, and temporal drift. This work introduces the RAFIQ NeuroGuardian™ framework, a conceptual and mathematical model that reframes neural decoding as a problem of adaptive alignment within evolving high-dimensional neural state spaces rather than static signal-to-output mapping. The central hypothesis is that neural representations are better described as trajectories within time-evolving manifolds, where information is encoded not solely in instantaneous signal features but in the geometry and temporal structure of population-level dynamics. This framework proposes a shift from channel-centric and feature-engineering approaches toward structure-aware modeling grounded in dynamical systems theory, information geometry, and high-dimensional representation theory. This document is explicitly non-implementational and non-clinical, and does not describe a functional device, algorithmic deployment, or medical intervention. Its purpose is to establish a formal theoretical baseline and timestamped scientific disclosure intended for future research, peer evaluation, and intellectual property precedence.