System and Method for Geometrodynamic State-Space Mapping from Multimodal Physiological Time-Series Data

Abstract A system and method for mapping a subject's neuro-physiological state onto a high-dimensional geometric manifold are disclosed. The system comprises a wearable sensor module, physically and communicatively coupled to a mobile computing device. The module includes a plurality of sensors for acquiring synchronous, multi-modal physiological time-series data, including at least electroencephalography (EEG), electrodermal activity (EDA), and heart rate variability (HRV) data. The method involves processing said data through a computational pipeline that first reconstructs a high-dimensional state-space representation of the subject's dynamics using time-delay embedding. Subsequently, topological data analysis, specifically persistent homology, is applied to the reconstructed state-space to compute a set of topological features. These features are then used to generate a quantitative map of a predefined 11-dimensional geometric structure, termed the General Reality Manifold (GRM), thereby providing an objective characterization of the subject's mental and physiological state for diagnostic and therapeutic applications.