For subjects reporting that they experienced workload variation between the different scenarios, the DFHM-workload index yielded significant differences between traffic-load levels and priority-flight request conditions. The DFHM-workload index gave stable results with highly significant correlations between scenarios with similar traffic-load conditions ( r between 0.671 and 0.809, p ≤ 0.001). The workload was assessed using the EEG-based DFHM-workload index and instantaneous self-assessment questionnaire. Mental workload variations were achieved by simulation scenarios with different number of aircraft and the occurrence of a priority-flight request as an exceptional event. In our study, we examined 21 air traffic controllers during arrival management tasks. However, validation of the method regarding reliability and reproducibility of results under realistic settings and real world scenarios was still required. The method was tested and validated already under laboratory conditions. For measuring mental workload using the EEG, we have developed the method of Dual Frequency Head Maps (DFHM). This is particularly relevant in safety-critical occupations, such as air traffic control. Optimal workload conditions are important for assuring employee's health and safety of other persons. In our digitized society, advanced information and communication technology and highly interactive work environments impose high demands on cognitive capacity.
3Signal Processing and Pattern Recognition, Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany.2Institute of Flight Guidance, German Aerospace Center, Braunschweig, Germany.1Mental Health and Cognitive Capacity, Work and Health, Federal Institute for Occupational Safety and Health, Berlin, Germany.Thea Radüntz 1 *, Norbert Fürstenau 2, Thorsten Mühlhausen 2 and Beate Meffert 3
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