A Neuroadaptive Cognitive Deployment Framework for Human–System Integration

Abstract

Human–system engineering has traditionally evaluated operator performance using metrics derived from workload, attention, or fatigue. While useful, these measures do not capture the dynamic neural processes through which cognitive resources are activated, stabilized, regulated, and recovered during task performance. This paper introduces the Neuroadaptive Cognitive Deployment Framework, which conceptualizes cognition as a dynamically deployable system resource within adaptive human–machine environments. The framework integrates baseline neural architecture estimation, derived from resting-state EEG and normative reference models, with real-time neural state monitoring to estimate cognitive deployability: the moment-to-moment readiness of neural systems to engage task-relevant processing under changing operational demands. A mathematical formulation of deployability is presented, together with a closed-loop system architecture that incorporates deployability estimates into adaptive control of human–system interaction. To characterize the temporal dynamics of engagement, the paper introduces the Cognitive Deployment Curve, which models transitions across baseline readiness, activation, sustained engagement, overload threshold, and recovery phases. By treating cognition as a deployable resource rather than a static capability, the framework extends traditional workload-based models and provides a foundation for neuroadaptive technologies in aviation, emergency response, complex engineering systems, and cognitive readiness training.

Keywords: Neuroergonomics, Human–System Integration, Cognitive Deployability, EEG, Adaptive Systems, Closed-Loop Control

DOI: 10.54941/ahfe1008214