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Related Experiment Video

Updated: Jan 29, 2026

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Characterizing Spatial Disorientation in Flight Simulation Through Electroencephalogram Trends.

Brock P Rouser, Logan D Rock, Ryan C Paul

    Aerospace Medicine and Human Performance
    |January 27, 2026
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    Summary
    This summary is machine-generated.

    Physiological data, including heart rate and EEG, can detect spatial disorientation (SD) in pilots during flight simulation. This finding aids in developing new safety measures against SD, a significant aviation risk.

    Keywords:
    Bayesian modelingGaussian modelingfixed-wingflight simulationphysiological monitoring

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    Area of Science:

    • Aviation Psychology and Human Factors
    • Neuroscience and Cognitive Science
    • Physiological Monitoring and Signal Processing

    Background:

    • Spatial disorientation (SD) poses a critical risk to aviation safety, especially during transitions to instrument meteorological conditions.
    • Investigating the physiological and cognitive impacts of induced SD illusions in simulated flight is crucial for enhancing pilot safety.

    Purpose of the Study:

    • To investigate the physiological and cognitive effects of induced spatial disorientation (SD) during simulated flight.
    • To explore the utility of multimodal sensor data, including electroencephalogram (EEG) and cardiorespiratory signals, for detecting SD.

    Main Methods:

    • Five instrument-rated pilots underwent simulated flights with decoupled motion cues to induce SD.
    • Collected physiological data included electroencephalogram (EEG) and cardiorespiratory monitoring (heart rate, respiratory rate).
    • Subjective measures of cognitive load and situational awareness were assessed using NASA Task Load Index and Situational Awareness Rating Technique.

    Main Results:

    • Increased heart rate (0.87 normalized) and respiratory rate (0.46 normalized) indicated sympathetic activation during SD.
    • Asymmetrical changes in F3 and F4 EEG channels (up to 0.86 normalized power difference) were observed during disorientation.
    • Pilot performance metrics alone were insufficient to reliably infer disorientation, highlighting the need for physiological data.

    Conclusions:

    • Physiological data, particularly EEG and cardiorespiratory signals, effectively capture state changes associated with spatial disorientation.
    • These findings support the development of adaptive, low-intervention countermeasures for SD.
    • A human-centered, data-driven approach using a scalable framework can improve SD detection and mitigation strategies in aviation.