Event Abstract

Back to Event

Tracking difficulty in a helicopter simulator: EEG complexity as a marker for mental workload

Andreas T. Poulsen1*, Jean-maurice Leonetti2, Lars Kai Hansen1 and Sid Kouider2
  • 1 Technical University of Denmark, Denmark
  • 2 École Normale Supérieure, France

Electroencephalography (EEG) is an intricate multi-dimensional measure, and there are many approaches to analyse EEG or compare it with the stimuli subjects are interacting with. Recent research has shown that the complexity of EEG signals is correlated with the levels of consciousness in comatose patients (Casali et al., 2013) as well as healthy subjects under anaesthesia and during sleep (Schartner et al.,2015; Andrillon et al., 2016; Schartner et al.,2017). In this study we investigate whether EEG complexity (LZc), as captured by the Lempel-Ziv algorithm (Lempel and Ziv, 1976), can be used in fully aware, healthy people as an index of how focused they are on a given task. Twenty subjects (hereof 10 were female) were recruited for an experiment, where they had to use a helicopter simulator to navigate through courses with varying difficulty with the aim of flying through circles. While the subjects interacted with the simulator, we recorded their EEG in order to investigate whether their neural activity reflected their performance of navigating the helicopter, as well as the varying difficulty of the simulator. This paradigm contained a higher degree of movement than normally seen in experiments where EEG is recorded. EEG can be sensitive to movement artefacts, which in our experiment might create false positives for difficult trials, due to the subject unintentionally moving their entire body. We therefore implemented an aggressive preprocessing using independent component analysis (ICA), followed by dipole-fitting and automatic classification of the ICs using plug-ins for the EEGLAB toolbox, to help identify non-cortical sources and remove them from the EEG (Delorme and Makeig, 2004; Oostenvelt et al., 2003; Frølich et al., 2015). The helicopter simulator was designed for this experiment and featured three modes with different ways to navigate the helicopter, as well as three different ways to wary the difficulty. By contrasting each of these difficulty types, we could investigate whether LZc was able to identify trials with high difficulty, where the subjects were assumed to struggle more. Furthermore, by contrasting the trials, where subjects were successful in navigating the helicopter, with failed trials, we could identify moments of high mental workload and investigate how well LZc was able to track these moments. Though not distinguishable on a single-trial level, subjects showed significantly higher complexity on average in the seconds before failing a trial compared to when they successfully navigated the helicopter through the circles (see figure 1). Additionally, a significant drop in complexity was measured in the navigational mode, which subjects reported as being the easiest. This mode was presumably the one they improved the most in, thereby reaching a plateau in their improvement early on. This could result in a decrease in focus, reflected in the decreased LZc. The difficulty type that obtained the highest LZc contrast between easy and hard trials, was also the difficulty type that was the most influential in whether subjects failed a trial. This was also the case when calculating this difficulty contrast only on successful trials, which indicates that the LZc not only captures neural changes up to a failure, but also when a subject is struggling during a hard but successful trial. That LZc is able to distinguish moments of varying workload consistently across subjects, suggests that EEG complexity is a viable candidate as an index of mental workload.

Figure 1
Image

References

Andrillon, T., Poulsen, A. T., Hansen, L. K., Léger, D., and Kouider, S. (2016). Neural Markers of Responsiveness to the Environment in Human Sleep. The Journal of Neuroscience, 36(24):6583–6596.

Casali, a. G., Gosseries, O., Rosanova, M., Boly, M., Sarasso, S., Casali, K. R., Casarotto, S., Bruno, M.-a., Laureys, S., Tononi, G., and Massimini, M. (2013). A Theoretically Based Index of Consciousness Independent of Sensory Processing and Behavior. Science Translational Medicine, 5(198):1–10.

Delorme, A. and Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1):9–21.

Frølich, L., Andersen, T. S., and Mørup, M. (2015). Classification of independent components of EEG into multiple artifact classes. Psychophysiology, 52(1):32–45.

Lempel, A. and Ziv, J. (1976). On the Complexity of Finite Sequences. IEEE Transactions on Information Theory, 22(1):75–81.

Oostenvelt, R., Delorme, A., and Makeig, S. (2003). DIPFIT: Equivalent dipole source localization of independent components.

Schartner, M., Seth, A., Noirhomme, Q., Boly, M., Bruno, M. A., Laureys, S., and Barrett, A. (2015). Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia. PLoS ONE, 10(8):1–21.

Schartner, M. M., Pigorini, A., Gibbs, S. A., Arnulfo, G., Sarasso, S., Barnett, L., Nobili, L., Massimini, M., Seth, A. K., and Barrett, A. B. (2017). Global and local complexity of intracranial EEG decreases during NREM sleep. Neuroscience of Consciousness, (September 2016):1–12

Keywords: EEG, complexity measure, neural markers, Mental Workload, Performance monitoring, level of consciousness

Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.

Presentation Type: Poster Presentation

Topic: Neuroergonomics

Citation: Poulsen AT, Leonetti J, Hansen L and Kouider S (2019). Tracking difficulty in a helicopter simulator: EEG complexity as a marker for mental workload. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00090

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 02 Apr 2018; Published Online: 27 Sep 2019.

* Correspondence: Mr. Andreas T Poulsen, Technical University of Denmark, Kongens Lyngby, Denmark, atpo@dtu.dk