• Project number: F 2402
• Institution: Federal Institute for Occupational Safety and Health (BAuA) / German Aerospace Center (DLR), Institute of Flight Guidance, Braunschweig
• Status: Completed Project

Description:

Inappropriate mental workload has short and long-term consequences. It is therefore desirable to intervene early in order to prevent them.

The registration of brain activity using electroencephalography has the potential to contribute significantly to the identification of inappropriate workload due to high cognitive demands. An electroencephalogram (EEG) depicts changes in activity in the cerebral cortex and subcortical structures and can be divided into a number of “frequency bands”. In particular, changes in the alpha and theta bands have been discussed as potential indicators of workload because, for example, the theta band responds reproducibly to tasks with different degrees of difficulty, which equate to different levels of demands.

Accordingly, the newly developed dual-frequency head maps (DFHMs) take into consideration both alterations of the alpha as well as the theta band. This workload indicator has already been tested in a laboratory setting. Consequently, the DFHM workload index now has to be validated for field use, employing innovative wireless and dry-electrode EEG registration technologies.

The project began with an assessment of commercially available systems for the recording of EEGs. Two main aspects were considered in each case: Firstly, the devices’ signal quality, which is crucial if the DFHM method is to be applied in the field. Secondly, how comfortable the devices are and what they look like, which influences the users’ willingness to wear them. The results indicate that most devices have satisfactory signal quality and meet with user acceptance. A gel-based, wireless EEG device delivered the best results for mobile use.

For the assessment of the new DFHM workload registration method under realistic conditions, aircraft arrival management was chosen. Simulating the air traffic control environment made it possible to create authentic scenarios in which various levels of workload could be generated. The results demonstrate that the EEG-based workload indicator accurately reflects the intensity of cognitive loads.

Since the DFHM index measures the amount of workload employees are exposed to, it will be a useful instrument for further studies on work design for situations characterised by cognitive load.