Measuring Correlates of Mental Workload During Simulated Driving Using cEEGrid Electrodes: A Test–Retest Reliability Analysis

Getzmann, Stephan; Reiser, Julian E.; Karthaus, Melanie; Rudinger, Georg; Wascher, Edmund · 2021 · Crossref

DOI: 10.3389/fnrgo.2021.729197

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Summary

This study investigates the test–retest reliability of cEEGrid electrodes for measuring mental workload correlates during simulated driving. While conventional EEG methods offer high reliability in laboratory settings, they lack ecological validity due to their intrusiveness. Conversely, modern mobile EEG technologies like cEEGrids are unobtrusive and suitable for real-world applications but have limited data regarding their longitudinal reliability. The authors aimed to determine whether cEEGrid measurements of alpha and theta frequency bands, along with behavioral driving parameters, remain consistent over long intervals, thereby assessing the technology's suitability for diagnostic or monitoring purposes in naturalistic environments. The researchers conducted a longitudinal analysis using data from 127 older adults (mean age 72.2 years) who participated in a large-scale study on driving abilities. Participants completed a 30-km simulated driving course twice, with an interval of approximately 13 months (range: 12–15 months). The driving scenario included varied road sections classified into simple, complex, and interactive profiles to induce different levels of mental workload. EEG data were recorded using cEEGrid electrodes positioned around the ears, capturing oscillatory power in the theta (3–6 Hz) and alpha (7–10 Hz) bands. Behavioral metrics included driving speed and steering wheel angular velocity. The analysis employed two approaches: a task-load related analysis comparing group-level responses to driving profiles across time points, and an intra-individual correlational analysis assessing the consistency of EEG and behavioral patterns for each participant between the two measurement sessions. The results demonstrated that group-level responses to driving complexity were highly consistent across both measurement points. As expected, participants drove faster and exhibited lower steering angular velocity during simple passages compared to complex or interactive ones. Similarly, EEG data showed that relative alpha power decreased and theta power increased during complex driving sections, reflecting higher mental workload. These patterns replicated the findings from the initial measurement, confirming the ecological validity of cEEGrids in capturing workload-related neural modulations. However, at the intra-individual level, test–retest reliability was low. Only about two-thirds of participants showed significant correlations between their EEG measures at the two time points, with most correlations being small to moderate. A significant portion of participants exhibited deviations in their neural responses to identical driving conditions over the 13-month interval. The study concludes that while cEEGrid electrodes provide satisfactory reliability and ecological validity for group-level analyses of driving-related mental workload, their low intra-individual test–retest reliability challenges their utility for individual diagnostic purposes. The findings suggest that transient fluctuations in mental states, such as alertness and vigilance, which are difficult to control in naturalistic settings, contribute to this variability. Consequently, cEEGrids are well-suited for research applications examining general cognitive processes during driving but may not be reliable for tracking individual changes in mental workload over long periods without additional controls.

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Topics

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• neuro workload indices
• workload measurement
• mental demand
• cognitive capacity variation
• stress driving

Information type

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• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics, tool software