NOCI-COMPILER: A THEORETICAL ARCHITECTURE FOR THE SEMANTIC TRANSLATION OF NOCICEPTIVE SIGNALS INTO A DIGITAL PAIN ALPHABET

Objectively quantifying pain remains a critical gap in biomedical engineering. Current assessment methods relyheavily on subjective self-reporting (e.g., VAS), yielding low-resolution data that fails to meet the rigorous criteria ofthe Pain Assessment Methods Evaluation Scale (P.A.M.E.S.). This paper introduces the "Noci-Compiler," a novelBrain-Computer Interface (BCI) architecture designed to translate raw neurophysiological signals into standardizedclinical data. We hypothesize that nociceptive transmission follows a decipherable syntax, where spike trains from Adelta and C fibers act as biological tokens. We propose a middleware system utilizing Spiking Neural Networks(SNNs) to "transpile" these signals into a structured "Digital Pain Alphabet." The architecture is validated via proof ofconcept in silico simulation using a Leaky Integrate-and-Fire (LIF) model, demonstrating the successful classificationof acute (40Hz) versus chronic (18Hz) signals. This formalism provides a theoretical framework for futureP.A.M.E.S.-compliant pain quantification systems