Non-Invasive Physiological Metrics for Cognitive Load Assessment in Training and Operational Contexts: Signal Processing, Evidence, and Feasibility

Cognitive load is a key determinant of performance, safety, and learning in high-stakes environments. Self-report and performance-based methods remain valuable but often miss rapid within-task changes in mental demand, motivating non-invasive physiological sensing for continuous monitoring. The interpretability of these signals depends on their specificity, acquisition quality, signal processing requirements, and real-world feasibility. Integrating physiological mechanism, measurement performance, and operational feasibility, this article reviews non-invasive physiological metrics, including cardiovascular measures (HR/HRV), respiratory metrics, EEG, fNIRS, ocular metrics, and electrodermal activity, for cognitive load assessment across training, simulated, and operational contexts. This structured narrative review synthesized 37 sources published between January 2021 and February 2026, including 25 primary empirical studies and 12 systematic reviews or meta-analyses identified through Google Scholar, PubMed, Scopus, Web of Science, and IEEE Xplore. Among the primary studies, cardiovascular measures were most frequently used (HR/HRV, 16 of 25), followed by EEG (11), ocular metrics (9), electrodermal activity (8), fNIRS (3), and respiratory metrics (3). A consistent trade-off emerged between physiological specificity and ease of deployment. EEG frontal theta showed the most direct and meta-analytically supported link to cortical processing, but it is constrained outside controlled settings by motion artifacts and setup demands. Cardiovascular and electrodermal signals deploy easily through wearables but reflect broader autonomic or sympathetic activation rather than cognitive load specifically. No single metric reviewed here provides both high specificity and strong field readiness. A more defensible approach pairs signals deliberately, based on complementary mechanisms, signal-processing burden, and deployment context, rather than adding sensors indiscriminately. A tiered decision framework and an iterative, context-aware synthesis are proposed to guide metric selection and the interpretation of complementary measurements over time.