PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Improving human performance in a real operating environment through real-time mental workload detection
Jens Kohlmorgen, Guido Dornhege, Mikio Braun, Benjamin Blankertz, Klaus-Robert Müller, Gabriel Curio, Konrad Hagemann, Andreas Bruns, Michael Schrauf and Wilhelm Kincses
In: Toward Brain-Computer Interfacing (2007) MIT Press , Cambridge, MA , pp. 409-422. ISBN 978-0-262-04244-4


The ability to directly detect mental over- and under-load in human operators is an essential feature of complex monitoring and control processes. Such processes can be found, for example, in industrial production lines, in aviation, as well as in common everyday tasks such as driving. In this chapter, we present an EEG-based system that is able to detect high mental workload in drivers operating under real traffic conditions. This information is used immediately to mitigate the workload typically induced by the influx of information that is generated by the car’s electronic systems. Two experimental paradigms were tested: an auditory workload scheme and a mental calculation task. The result is twofold. The system’s performance is strongly subject-dependent; however, the results are good to excellent for the majority of subjects. We show that in these cases an induced mitigation of a reaction time experiment leads to an increase of the driver’s overall task performance.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:3321
Deposited By:Benjamin Blankertz
Deposited On:07 February 2008