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Non-Invasive Physiological Metrics for Cognitive Load Assessment in Training and Operational Contexts: Signal
Mowffq M Alsanousi1,2, Vittaldas V Prabhu1
1Industrial and Manufacturing Engineering Department, Pennsylvania State University, State College, PA 16802, USA.
Sensors (Basel, Switzerland)
|August 13, 2026
Summary
Assessing cognitive load requires balancing physiological specificity with practical deployment. No single non-invasive metric is ideal; combining complementary measures offers the most robust approach for real-world applications.
Area of Science:
- Human Factors Engineering
- Neuroscience
- Physiological Monitoring
Background:
- Cognitive load significantly impacts performance and learning in critical situations.
- Traditional assessment methods often fail to capture dynamic changes in mental workload.
- Non-invasive physiological sensing offers continuous monitoring potential.
Purpose of the Study:
- To review non-invasive physiological metrics for cognitive load assessment.
- To evaluate metrics based on physiological mechanisms, measurement performance, and operational feasibility.
- To guide the selection and interpretation of physiological signals for cognitive load.
Main Methods:
- Structured narrative review of 37 sources (25 primary studies, 12 reviews/meta-analyses) published Jan 2021-Feb 2026.
- Searched major academic databases (Google Scholar, PubMed, Scopus, Web of Science, IEEE Xplore).
- Synthesized findings on cardiovascular, respiratory, EEG, fNIRS, ocular, and electrodermal metrics.
Main Results:
- Cardiovascular measures (HR/HRV) were most common (16/25 studies), followed by EEG (11), ocular (9), electrodermal (8), fNIRS (3), and respiratory (3).
- A trade-off exists between physiological specificity and ease of deployment.
- EEG frontal theta shows strong cortical links but is sensitive to artifacts; wearables (cardiovascular, electrodermal) are deployable but less specific.
Conclusions:
- No single non-invasive metric offers both high specificity and field readiness for cognitive load assessment.
- Combining complementary physiological signals, based on context and mechanism, is recommended.
- A tiered, iterative framework can guide metric selection and interpretation for robust cognitive load monitoring.
