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Real-time state estimation in a flight simulator using fNIRS.

Thibault Gateau¹, Gautier Durantin¹, Francois Lancelot¹

¹ISAE (Institut supérieur de l'aéronautique et de l'espace), Toulouse, France.

|March 28, 2015

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This study developed a brain-computer interface using functional near-infrared spectroscopy (fNIRS) to monitor pilots' working memory load during flight simulations. The system accurately identified pilot mental states and task difficulty, enhancing flight safety potential.

Area of Science:

Neuroscience
Human-Computer Interaction
Aerospace Engineering

Background:

Working memory is crucial for pilots, especially when recalling air traffic control instructions.
Limitations in working memory can compromise flight safety.
Functional near-infrared spectroscopy (fNIRS) shows potential for assessing cognitive load.

Purpose of the Study:

To implement an on-line fNIRS-based inference system for assessing pilot working memory load.
To integrate two complementary estimators: a real-time state estimation algorithm and an on-line SVM-based classifier.
To evaluate the system's effectiveness in a realistic flight simulator environment.

Main Methods:

Utilized functional near-infrared spectroscopy (fNIRS) to monitor brain activity.

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Last Updated: Apr 15, 2026

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

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fMRI Validation of fNIRS Measurements During a Naturalistic Task

Published on: June 15, 2015

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Developed a MACD-based algorithm for real-time pilot mental state estimation (on-task vs. not-on-task).

Employed a Support Vector Machine (SVM) classifier to differentiate between low and high working memory load tasks.

Main Results:

The mental state estimator achieved 62% global accuracy, 58% specificity, and 72% sensitivity.
The working memory load classifier demonstrated 80% accuracy, 72% specificity, and 89% sensitivity.
Both estimators proved effective in a flight simulator with 19 pilots recalling instructions.

Conclusions:

The developed fNIRS-based system can effectively infer pilot mental state and working memory load.
These estimators serve as foundational components for future passive brain-computer interface development in aviation.
The findings suggest a pathway to enhance flight safety through real-time cognitive monitoring.