sensors Article Tracking of Mental Workload with a Mobile EEG Sensor Ekaterina Kutafina 1,2,* , Anne Heiligers 3, Radomir Popovic 1 , Alexander Brenner 4, Bernd Hankammer 1, Stephan M. Jonas 5, Klaus Mathiak 3 and Jana Zweerings 3 1 Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany;
[email protected] (R.P.);
[email protected] (B.H.) 2 Faculty of Applied Mathematics, AGH University of Science and Technology, 30-059 Krakow, Poland 3 Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, 52074 Aachen, Germany;
[email protected] (A.H.);
[email protected] (K.M.);
[email protected] (J.Z.) 4 Institute of Medical Informatics, University of Münster, 48149 Münster, Germany;
[email protected] 5 Department of Informatics, Technical University of Munich, 85748 Garching, Germany;
[email protected] * Correspondence: ekutafi
[email protected] Abstract: The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self- mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load.