Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications

TLDR: This study designed a self-paced BCI and proposed an interaction technique which enables the user to send high-level commands and suggested that a user could explore a virtual museum faster with this interaction technique than with current techniques.

Abstract: A Brain-Computer Interface (BCI) is a communication system which enables its users to send commands to a computer by using brain activity only, this brain activity being measured, generally by ElectroEncephaloGraphy (EEG), and processed by the system. In the first part of this thesis, dedicated to EEG signal processing and classification techniques, we aimed at designing interpretable and more efficient BCI. To this end, we first proposed FuRIA, a feature extraction algorithm based on inverse solutions. This algorithm can automatically identify relevant brain regions and frequency bands for classifying mental states. We also proposed and studied the use of Fuzzy Inference Systems (FIS) for classification. Our evaluations showed that FuRIA and FIS could reach state-of-the-art results in terms of classification performances. Moreover, we proposed an algorithm that uses both of them in order to design a fully interpretable BCI system. Finally, we proposed to consider self-paced BCI design as a pattern rejection problem. Our study introduced novel techniques and identified the most appropriate classifiers and rejection techniques for self-paced BCI design. In the second part of this thesis, we focused on designing virtual reality (VR) applications controlled by a BCI. First, we studied the performances and preferences of participants who interacted with an entertaining VR application, thanks to a self-paced BCI. Our results stressed the need to use subject-specific BCI as well as the importance of the visual feedback. Then, we developed a VR application which enables a user to explore a virtual museum by using thoughts only. In order to do so, we designed a self-paced BCI and proposed an interaction technique which enables the user to send high-level commands. Our first evaluation suggested that a user could explore the museum faster with this interaction technique than with current techniques.