Monitoring Pilot's Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight...

Area of Science:

• Neuroscience
• Human-Computer Interaction
• Aerospace Engineering

Background:

• Advancements in low-cost, portable brain sensors (dry-electrode EEG) enable cognitive activity monitoring outside laboratory settings.
• This technology offers potential for real-world applications, such as monitoring pilots during flight operations.
• Benchmarking these sensors in operational conditions is crucial for their practical implementation.

Purpose of the Study:

• To evaluate the effectiveness of dry-electrode electroencephalography (EEG) systems for monitoring pilot cognitive load in a realistic flight scenario.
• To assess the performance of EEG in distinguishing between low and high cognitive load conditions during flight tasks.
• To determine the potential and limitations of current dry-EEG technology for operational use in aviation.

Main Methods:

• Twenty-two pilots performed low and high cognitive load traffic patterns using a six-dry-electrode EEG system.
• Cognitive load was manipulated by having pilots either monitor or fly the aircraft.
• An auditory oddball task was included, and EEG data (event-related potentials and frequency bands) were analyzed.

Main Results:

• Higher P300 amplitude and specific alpha and theta band power were observed in the low load condition compared to the high load condition.
• Single trial classification accuracy using combined EEG features did not exceed chance level.
• Classification accuracy reached approximately 70% when using only frequency features from the continuous EEG signal.

Conclusions:

• Dry-EEG technology shows potential for monitoring pilot cognition in complex, noisy, and ecological environments like aircraft cockpits.
• Current dry-EEG hardware requires improvement for reliable, everyday use in flight operations.
• Frequency-based EEG features demonstrate utility in discriminating cognitive load levels during flight.