Section: New Results

Understand and modeling Mental-Imagery BCI user training

Participants: Camille Benaroch, Aline Roc, Léa Pillette, Fabien Lotte

External collaborators: Camille Jeunet, Bernard N'Kaoua

Computational models of performance: Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) make use of brain signals produced during mental imagery tasks to control a computerised system. The currently low reliability of MI-BCIs could be due, at least in part, to the use of inappropriate user-training procedures. In order to improve these procedures, it is necessary first to understand the mechanisms underlying MI-BCI user-training, notably through the identification of the factors influencing it. Thus, we first aimed at creating a statistical model that could explain/predict the performances and the progression of MI-BCI users using their traits (e.g., personality). We used the data of 42 participants (i.e., 180 MI-BCI sessions in total) collected from three different studies that were based on the same MI-BCI paradigm. We used machine learning regressions with a leave-one-subject-out cross validation to build different models. A first results showed that using the users' traits only may enable the prediction of performances for a single multiple-session experiment, but might not be sufficient to reliably predict MI-BCI performances across different experiments. A second result showed that using the users' traits and the users' past performances may enable the prediction of the progression of one user as reliable models were found for two of the three studies. Part of this work was published at the International Graz BCI conference in [21].

Would Motor-Imagery based BCI user training benefit from more women experimenters?: Throughout MI-BCI use, human supervision (e.g., experimenter or caregiver) plays a central role. While providing emotional and social feedback, people present BCIs to users and ensure smooth users' progress with BCI use. Though, very little is known about the influence experimenters might have on the results obtained. Such influence is to be expected as social and emotional feedback were shown to influence MI-BCI performances. Furthermore, literature from different fields showed an experimenter effect, and specifically of their gender, on experimental outcome. We assessed the impact of the interaction between experimenter and participant gender on MI-BCI performances and progress throughout a session. Our results revealed an interaction between participants gender, experimenter gender and progress over runs. It seems to suggest that women experimenters may positively influence participants' progress compared to men experimenters. This work was published at the International Graz BCI conference in [28].