Real-Time Fault Anticipation in brain-machine interface Systems Using Surcognitive Inference Engines

As Brain-Machine Interfaces (BMIs) advance toward clinical integration, ensuring cognitive stability under uncertainty has become a foundational requirement. This work departs from conventional decoder-centric paradigms by introducing a cognitively introspective architecture that reasons through its own instability.This study introduces a modular neuro-symbolic inference engine designed to anticipate internal divergences in user intent—referred to as cognitive faults—before they manifest as behavioral errors.The system comprises six interpretable modules, each dedicated to resolving distinct dimensions of epistemic conflict, from hypothesis generation and contradiction detection to arbitration and controlled reset. These modules operate under a dynamic meta-inference layer capable of introspecting symbolic traces and triggering adaptive reconfiguration.Evaluations conducted across three high-fidelity simulated BMI contexts—motor imagery decoding, attentional drift, and inhibitory control failure—demonstrated robust early fault detection, achieving sub-30 ms intervention latency and 92.4% accuracy under rare-event conditions.Beyond detection, the framework supports symbolic traceability and schema-based introspection, ensuring interpretability and system accountability. Its architecture is implementation-ready for neuromorphic substrates and aligns with emerging cognitive safety standards. Rather than post hoc explainability, this model embeds real-time epistemic transparency as a structural principle.This approach offers a viable pathway for embedding self-corrective inference mechanisms in BMI systems, contributing to the development of cognitively aligned and resilient human–machine interaction.