Shining light on the learning brain: Estimating mental workload in a simulated flight task using optical f-NIRS signals
Accurately estimating cognitive workload is essential for adaptive learning in pilot training, allowing flight simulation tasks to be tailored to the pilot’s skill level. In this project, we investigate the use of functional near-infrared spectroscopy (f-NIRS) to measure cognitive workload in pilots during a simulated flight task. f-NIRS, a non-invasive brain imaging technique, monitors changes in blood oxygenation in the cerebral cortex. Participants performed a series of flight maneuvers varying in complexity, whilst f-NIRS, breathing, and electrocardiography (ECG) signals were measured. Workload levels were quantified using both objective (task performance metrics) and subjective (self-reported workload) measures. To predict objective workload levels from the f-NIRS signals, we discarded noisy signals, extracted useful features from the signals, and fed these to machine learning predictors. Our predictor performs mental workload estimation in an objective and subject-independent manner. Ultimately, we demonstrate the viability of fNIRS as a tool for real-time workload monitoring in aviation, with a potential application in adaptive pilot training.
- Date:
- Speakers:
- Elisabeth Heremans
- Affiliation:
- Microsoft Research Intern