Tracking of Mental Workload with a Mobile EEG Sensor

Sensors, vol. 21, pp. 5205

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. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

Authors 8

1. Ekaterina Kutafina corresponding Aachen Institute of Medical Informatics Medical Faculty

   AGH University of Krakow · RWTH Aachen University

   Affiliation as printed

   Faculty of Applied Mathematics, AGH University of Science and Technology, 30-059 Krakow, Poland

   Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

2. Anne Heiligers Aachen Department of Psychiatry, Psychotherapy and Psychosomatics School of Medicine

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, 52074 Aachen, Germany

3. Radomir Popović Aachen Institute of Medical Informatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

4. Alexander Brenner

   University of Münster

   Affiliation as printed

   Institute of Medical Informatics, University of Münster, 48149 Münster, Germany

5. Bernd Hankammer Aachen Institute of Medical Informatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany

6. Stephan Jonas

   Technical University of Munich

   Affiliation as printed

   Department of Informatics, Technical University of Munich, 85748 Garching, Germany

7. Klaus Mathiak Aachen Department of Psychiatry, Psychotherapy and Psychosomatics School of Medicine

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, 52074 Aachen, Germany

8. Jana Zweerings Aachen Department of Psychiatry, Psychotherapy and Psychosomatics School of Medicine

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, School of Medicine, RWTH Aachen University, 52074 Aachen, Germany