Understanding & Improving Mental-Imagery Based Brain-Computer Interface (Mi-Bci) User-Training : towards A New Generation Of Reliable, Efficient & Accessible Brain- Computer Interfaces

Abstract : Mental-imagery based brain-computer interfaces (MI-BCIs) enable users to interact with theirenvironment using their brain-activity alone, by performing mental-imagery tasks. This thesisaims to contribute to the improvement of MI-BCIs in order to render them more usable. MIBCIsare bringing innovative prospects in many fields, ranging from stroke rehabilitation tovideo games. Unfortunately, most of the promising MI-BCI based applications are not yetavailable on the public market since an estimated 15 to 30% of users seem unable to controlthem. A lot of research has focused on the improvement of signal processing algorithms.However, the potential role of user training in MI-BCI performance seems to be mostlyneglected. Controlling an MI-BCI requires the acquisition of specific skills, and thus anappropriate training procedure. Yet, although current training protocols have been shown tobe theoretically inappropriate, very little research is done towards their improvement. Our mainobject is to understand and improve MI-BCI user-training. Thus, first we aim to acquire a betterunderstanding of the processes underlying MI-BCI user-training. Next, based on thisunderstanding, we aim at improving MI-BCI user-training so that it takes into account therelevant psychological and cognitive factors and complies with the principles of instructionaldesign. Therefore, we defined 3 research axes which consisted in investigating the impact of(1) cognitive factors, (2) personality and (3) feedback on MI-BCI performance. For each axis,we first describe the studies that enabled us to determine which factors impact MI-BCIperformance; second, we describe the design and validation of new training approaches; thethird part is dedicated to future work. Finally, we propose a solution that could enable theinvestigation of MI-BCI user-training using a multifactorial and dynamic approach: an IntelligentTutoring System.
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Camille Jeunet. Understanding & Improving Mental-Imagery Based Brain-Computer Interface (Mi-Bci) User-Training : towards A New Generation Of Reliable, Efficient & Accessible Brain- Computer Interfaces. Psychology. Université de Bordeaux, 2016. English. ⟨NNT : 2016BORD0221⟩. ⟨tel-01417606⟩

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