Human-Machine Interaction Assessment by Neurophysiological Measures: A Study on Professional Air Traffic Controllers

Area of Science:

• Human-Computer Interaction
• Neuroscience
• Air Traffic Management

Background:

• Assessing human-machine interaction (HMI) effectiveness is crucial for enhancing operator experience and safety in critical fields like Air Traffic Management (ATM).
• Traditional subjective workload assessments may not always accurately reflect operator cognitive load.
• Neurophysiological measures offer a potential objective method for evaluating HMI effectiveness.

Purpose of the Study:

• To investigate the utility of neurophysiological measures, specifically electroencephalography (EEG), for assessing HMI effectiveness in an ATM context.
• To compare the effectiveness of two auditory interaction modalities (Normal vs. Augmented) for aircraft location tasks.
• To determine if neurophysiological measures are more sensitive than subjective measures in detecting differences in workload and performance.

Main Methods:

• Ten professional air traffic controllers participated in a simulated ATM control tower environment.
• Participants performed an auditory aircraft localization task using two modalities: 'Normal' (uniform sound amplification) and 'Augmented' (adaptive sound amplification based on head orientation).
• Performance data, subjective workload (NASA-TLX), and EEG-based workload indices were collected.

Main Results:

• The 'Augmented' interaction modality resulted in significantly higher task performance compared to the 'Normal' modality.
• EEG-based workload indices showed a significant decrease in cognitive load with the 'Augmented' modality.
• A significant negative correlation was found between performance and EEG-based workload, while subjective workload measures showed no significant trend.

Conclusions:

• The augmented auditory display significantly enhances HMI effectiveness in the ATM domain, leading to improved performance and reduced cognitive load.
• Neurophysiological measures (EEG) are more sensitive and effective than subjective questionnaires (NASA-TLX) for assessing workload and HMI effectiveness.
• This study demonstrates the potential of employing EEG as a reliable objective metric for evaluating and optimizing human-machine systems.