A New Method for Mental Workload Registration
Complex and highly automated systems impose high demands on employees with respect to cognitive capacity and the ability to cope with workload. Objectively registering mental workload at workplaces with high cognitive demands would enable prevention of over- and underload. Although urgently needed, such technical measurement is currently unfeasible. Hence, the goal of this work is the establishment of precisely such an objective method.
In this article we briefly present a new method for registering mental workload by means of the electroencephalogram (EEG). Based on so called Dual Frequency Head Maps (DFHM) every 5 s we obtain an index of mental state ranging between the classes low, moderate, and high workload.
Finally, we present results from a sample set of 54 people during the execution of the cognitive tasks 0-back, stroop test and AOSPAN in a laboratory setting. We compare them with our expectations based on the knowledge of task requirements on the executive functions as well as with further workload relevant biosignal data, performance data, and the NASA-TLX as a subjective questionnaire method. By this we gain proof of the integrity of the new method.
KeywordsMental workload Electroencephalogram (EEG) Signal processing Pattern recognition
We would like to thank Dr Sergei Schapkin, Dr Patrick Gajewski, and Prof Michael Falkenstein for selection of the battery’s tasks. We would like to thank Mr Ludger Blanke for technical support during the timing tests for the tasks. In addition, we would like to thank Ms Xenija Weißbecker-Klaus, Mr Robert Sonnenberg, Dr Sergei Schapkin, and Ms Marion Freyer for general task testing and for conducting the laboratory experiments. Furthermore, we would like to thank Ms Marion Exner for daily operational support and our student assistant Jon Scouten for proofreading.
More information about the project where our EEG data were acquired can be found under the following link: http://www.baua.de/de/Forschung/Forschungsprojekte/f2312.html?nn=2799254.
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