The influence of voice on the brainwaves of pilots

Abstract

In aviation, air traffic control (ATC) is known as the most important safety net. Optimal performance assessment is key in the context of safety-critical behaviour. To achieve optimal performance, it is necessary to understand brainwave coherence from a human factors neuroergonomic perspective. As Electroencephalogram (EEG) signals reveal cortical electrical activity, brainwave coherence can be assessed using electrophysiological measures. This study explored ATC voice perception (VP) related to pilot brainwave coherence during three different ATC voice simulations. The cortical activity of 77 pilots was recorded with the 7-channel Muse EEG headset during a 22 second ATC simulation. The VP questionnaire was administered to each pilot after listening to the three ATC voices. To obtain the underlying VP factor structure of each voice, factor analysis was employed. Modelling the three VP factors explained brainwave variance more parsimoniously. Asking the question whether ATC VP affects brainwaves of pilots, ANOVA showed significant differences between the brainwave coherence of three ATC voices. Structural equation modelling (SEM) for ATC VP showed the high pitch female voice (A) contributed to higher delta and theta brainwaves which might contribute to cognitive fatigue and workload. The medium pitch female voice (B) showed an increase in theta, beta and gamma brainwaves across different time stamps. Beta and theta cross overs might be an indication of a possible startle effect. Although the low pitch male voice (C) showed the least co-variance and variance throughout the three different ATC voice conditions and time stamp cross overs, results showed an inverse relationship with beta brainwaves. A decrease in beta brainwaves is associated with calmness and a brainwave state associated with optimal performance. This VP study shows that real-time pilot CP can be decoded from EEG signals, using the mobile Muse headset. If ATM EEG data can be recorded under different operational conditions, results may be used to understand and monitor both ATC and pilot performance for aviation safety.