Correlations of pilot trainees' brainwave dynamics with subjective performance evaluations: insights from EEG microstate analysis

Zhao, Mengting; Law, Andrew; Su, Chang; Jennings, Sion; Bourgon, Alain; Jia, Wenjun; Larose, Marie-Hélène; Bowness, David; Zeng, Yong · 2025 · Crossref

DOI: 10.3389/fnrgo.2025.1472693

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Summary

This study investigates the neurophysiological correlates of aircraft control proficiency by examining the relationship between EEG microstate dynamics and subjective performance evaluations in pilot trainees. The research addresses a gap in understanding the cognitive mechanisms underlying aviation expertise, aiming to identify how specific brainwave patterns associate with different dimensions of flight control. This knowledge is intended to inform the design of targeted training interventions that enhance pilot skill development and safety. The experimental design involved 24 healthy participants (aged 21–41) who underwent a simulator-based pilot training process using a custom flight simulator modeled on a Boeing 737. Participants performed 22 sessions categorized into three stages: Training, Practice A, and Practice B. Each session included baseline tasks (maintaining straight and level flight) and trial tasks involving maneuvers of varying difficulty (climbs, descents, turns, and reversals). While participants controlled pitch and roll via a yoke, an autopilot managed speed and turn coordination. EEG data were recorded using a 64-channel BioSemi system. Data preprocessing included artifact rejection via MARA and spherical spline interpolation. EEG microstate analysis was performed using a modified k-means clustering algorithm on Global Field Power peaks, yielding seven global microstate classes (A–G). For each class, three parameters were computed: mean coverage, duration, and occurrence. Subjective performance was evaluated by an instructor across five dimensions: control-roll, performance-heading, control-pitch, performance-altitude, and performance-rate climb/descent. Statistical analysis included repeated measures ANOVA for microstate parameters and performance scores, and Spearman correlation coefficients to assess associations between microstate features and subjective evaluations. Results indicated that aircraft control proficiency is multidimensional, with significant main effects of evaluation dimension and training stage on performance scores. Notably, performance-altitude improved significantly from the Training to both Practice stages. Correlation analysis revealed distinct associations between specific microstate classes and control dimensions. Microstate classes E and G showed positive correlations with aircraft control, suggesting that attentional processes, perceptual integration, working memory, cognitive flexibility, and executive control are critical for aviation expertise. Conversely, microstate classes C and F exhibited negative correlations with control performance, indicating that the engagement of cognitive control networks (associated with class C) may be inversely related to efficient flight task execution in this context. These findings highlight that different cognitive processes contribute variably to different aspects of flight control. The significance of this study lies in its demonstration that EEG microstate analysis can serve as a neurophysiological marker for pilot training progress. By linking specific brainwave dynamics to subjective performance evaluations, the research provides a basis for developing data-driven, targeted pilot training programs. Understanding which cognitive states (e.g., attentional vs. cognitive control) correlate with successful maneuvering allows for the optimization of instructional methods and the design of interventions that specifically enhance the neural mechanisms underlying skilled aircraft control, ultimately contributing to improved aviation safety and operational efficiency.

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• neuro workload indices

Information type

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• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics