Physiological computing to assess and optimize 3D User Interfaces

Recently, physiological computing has been shown to be a promising companion to Human-Computer Interfaces (HCI) in general, and to 3D User Interfaces (3DUI) in particular, in several directions. Among them, in our group, we are first interested in using various physiological signals, and notably EEG signals, as a new tool to assess objectively the ergonomic quality of a given 3DUI, to identify where and when are the pros and cons of this interface, based on the user’s mental state during interaction. This is known as Neuroergonomics. For instance, estimating the user’s mental workload during interaction can give insights about where and when the interface is cognitively difficult to use. This could be useful for 2D HCI in general, and even more for 3DUI. Indeed, in a 3DUI, the user perception of the 3D scene – part of which could potentially be measured in EEG, is essential. Moreover, the usual need for a mapping between the user inputs and the corresponding actions on 3D objects make 3DUI and interaction techniques more difficult to assess and to design.
In this area of Neuroergonomics, we have notably shown that we could reliably measure mental workload from EEG signals, including during complex interaction tasks, such as 3D object manipulation tasks and navigation tasks. We have also shown that we could estimate visual comfort during stereoscopic displays within EEG signals.
Beyond evaluation alone, physiological computing could also improve existing 3DUI by increasing the symbiosis between the user and the interface, e.g., for visualization and analysis of large amounts of (3D) data.

Using EEG to assess the ergonomic qualities of 3D User Interfaces

Using EEG to assess the ergonomic qualities of 3D User Interfaces

Selected related publications:

  • J. Frey, M. Hachet, F. Lotte, “EEG-based Neuroergonomics for 3D User Interfaces: opportunities and challenges”, Le Travail Humain, 2017 – pdf
  • J Frey, M Daniel, J Castet, M Hachet, F Lotte, “Framework for Electroencephalography-based Evaluation of User Experience”, ACM SIGCHI Conference on Human Factors in Computing Systems (ACM CHI), 2016 – pdf
  • J. Frey, A. Appriou, F. Lotte, M. Hachet, “Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort”, Computational Intelligence and Neuroscience, Article ID 2758103, 2016 – pdf
  • D. Wobrock, J. Frey, D. Graeff, J.-B. de la Rivière, J. Castet, F. Lotte, “Continuous Mental Effort Evaluation during 3D Object Manipulation Tasks based on Brain and Physiological Signals”, Interact 2015 – pdf
  • C. Mühl, C. Jeunet, F. Lotte, “EEG-based Workload Estimation Across Affective Contexts”, Frontiers in Neurosciences section Neuroprosthetics, vol 8, no. 114, 2014 – pdf
  • C. Jeunet, F. Lotte, C. Mühl, “Design and Validation of a Mental and Social Stress Induction Protocol: Towards Load-Invariant Physiology-Based Detection”, International Conference on Physiological Computing Systems (PhyCS 2014), pp. 98-106, 2014 – pdf
  • J. Frey, C. Mühl, F. Lotte, M. Hachet. “Review of the Use of Electroencephalography as an Evaluation Method for Human-Computer Interaction”, International Conference on Physiological Computing Systems (PhyCS 2014), pp. 214-223, 2014 – pdf

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