La soutenance de thèse de Laurent George : vendredi 7 décembre 2012 à 14h à l’INRIA/IRISA Rennes, salle Métivier.
Cette thèse porte sur « l’étude des interfaces cerveau-ordinateur actives et passives pour interagir avec des environnements virtuels en se basant sur l’activité électroencéphalographique et la charge mentale ».
Composition du jury :
• CABESTAING François, Professeur des universités, Université de Lille [Rapporteur]
• NIJHOLT Anton, Professeur des universités, Université de Twente [Rapporteur]
• HACHET Martin, Chargé de recherches, INRIA
• MATTOUT Jérémie, Chargé de recherches, INSERM
• ARNALDI Bruno, Professeur des universités, INSA de Rennes
• LÉCUYER Anatole, Directeur de recherches, INRIA [Directeur de thèse]
Intitulé: « Contribution to the Study of Active and Passive Brain-Computer Interfaces for Interacting with Virtual Environments based on Electroencephalography and Mental Workload »
A Brain Computer Interface (BCI) is a communication system between a user and a computer in which the carried message is the measured user’s brain activity. In recent years, BCIs have been proposed to be used by healthy users. Indeed BCIs offer a novel kind of interaction « by thought » for different application contexts such as robotics, domotics, multimedia, virtual reality, and video games. In this work we focus on the use of BCIs for interacting with virtual environments. We also focus on the use of brain signals that are related to the user’s mental workload or closely related states.
First, we have studied the electroencephalography (EEG) markers and the BCI setups that can be used to read out in real-time the user mental states such as mental workload, concentration and relaxation states. We have designed and evaluated an active BCI system based on relaxation and concentration mental states. Our results suggest that using EEG scalp electrodes to detect relaxation and concentration states in active BCI context is feasible.
Then we have explored the use of « passive » BCIs to improve the use of an active BCI. In passive BCIs the user does not try to control his/her brain activity, and he/she can remain mainly concerned by his/her primary task. The brain activity is analyzed to read out the user mental state which is used to adapt the application. We have introduced the concept of the BCI inhibitor which can be defined as a passive BCI system that pauses the active BCI until the user’s « brain » is ready.
Then we have studied the use of passive BCIs based on mental workload in a virtual reality context to assist the user when a high workload is detected. To illustrate this approach we have designed a visuo-haptic virtual reality setup in which a haptic guiding system is automatically toggled based on mental workload during a path-following task. We have conducted an experiment to evaluate the operability and efficiency of the proposed system. Results suggest that the proposed passive BCI system is able to measure a mental workload index that seems well correlated with the difficulty of the task. Activation of guides based on the measured mental workload index allowed to increase task performances by significantly reducing the number of collisions.
We have finally studied how our approach could be applied to virtual reality and medical applications. We have proposed to use a passive BCI to adapt in real-time a medical simulator to the user’s mental states. If the user becomes too heavily engaged in the medical operation, some visual and haptic cues are automatically activated. We have conducted a pilot study to test the usability of the proposed system and to evaluate the influence of such a system on task performance. Preliminary results tend to show the operability and the usefulness of the proposed proof-of-concept. Users perceived the provided assistance as relatively accurate. The virtual cues were adapted in real-time according to the user’s mental workload, being updated every two seconds. This could pave the way to a future generation of « smart » medical simulator taking into account brain signals and mental states in training procedures.
Mots clés : BCI, passive BCI, mental workload, haptic feedback, EEG