A System and Method for Large-Scale Geometric Analysis of Aggregated Neurophenomenological Data

Abstract A system and method for analyzing large-scale, aggregated neurophenomenological data are disclosed. Prior art in neurotechnology is limited by a "semantic gap," failing to capture the holistic structure of subjective experience from neurophysiological signals.1 The present invention overcomes these limitations by providing a system comprising a network of remote sensor modules and a central analytics platform. The system receives neuro-data streams and transforms them into high-dimensional geometric objects, termed General Reality Manifolds (GRMs), which represent the holistic conscious state of each individual subject.1 These GRMs are subsequently anonymized and aggregated on the central platform. The platform performs geometric and topological analysis on the aggregated manifold data to identify population-level trends, correlations, and biomarkers related to collective cognitive and mental health, thereby providing a novel means of deriving semantic insights from neurophysiological data at a macro scale.1