Dr. Michael Knierim

Prolonged or excessive mental workload are linked to stress and lower productivity in everyday worklife. Unfortunately though, mental workload is hard to assess objectively as it develops through the balance of a persons abilities and task demands. An innovative way for workload monitoring could be the use of brain activity monitoring using EEG headphones (see, e.g. https://github.com/MKnierim/openbci-headphones). In this thesis project you will work with already collected data sets and develop machine learning models to predict mental workload levels across various tasks and settings.

I am quite interested in integrating your ideas in this topic, so please consider submitting them together with your CV & transcript of records when applying for the project.

Also, if you have any questions about the topic beforehand, please contact Michael Knierim (michael.knierim@kit.edu).

Additional Materials:

• Michael Thomas Knierim, Daniel Puhl, Gabriel Ivucic, and Tobias Röddiger. 2023. OpenBCI + 3D-Printed Headphones = Open ExG Headphones – An Open-Source Research Platform for Biopotential Earable Applications. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA ’23), April 23–28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3544549.3585875
• Bartholomeyczik, K., Knierim, M.T., Nieken, P., Seitz, J., Stano, F., Weinhardt, C. (2022). Flow in Knowledge Work: An Initial Evaluation of Flow Psychophysiology Across Three Cognitive Tasks. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A.B., Müller-Putz, G.R. (eds) Information Systems and Neuroscience. NeuroIS 2022. Lecture Notes in Information Systems and Organisation, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-031-13064-9_3
• https://www.microsoft.com/en-us/worklab/work-trend-index/brain-research