Abstract
This paper presents a system for live recognition of mental workload using spectral features from EEG data classified by Support Vector Machines. Recognition rates of more than 90% could be reached for five subjects performing two different cognitive tasks according to the flanker and the switching paradigms. Furthermore, we show results of the system in application on realistic data of computer work, indicating that the system can provide valuable information for the adaptation of a variety of intelligent systems in human-machine interaction.
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Heger, D., Putze, F., Schultz, T. (2010). Online Workload Recognition from EEG Data during Cognitive Tests and Human-Machine Interaction. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_47
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DOI: https://doi.org/10.1007/978-3-642-16111-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16110-0
Online ISBN: 978-3-642-16111-7
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