One category of factors that can be interesting to predict and explain Brain-Computer Interface (BCI) users' performances are the one based on user stable characteristic such as their personality profile, their learning style, their spatial abilities or their age and gender. In our experiments, subjects were asked to complete psychometric and personality questionnaires, which aimed to assess different aspects of their personality and cognitive profile. The results of those different questionnaires provided us with the explanatory variables to build our different models using traits of the user. In addition, in this study, we build statistical models across experiments as factors that have already been identified as predictors of BCI performance in the literature were studied in single experiments and/or data sets.
So far, our results suggest that, anxious users might have difficulties controlling a BCI while users with high spatial abilities have a tendency to perform better. Furthermore, it seems possible to predict the performance of a session N+1, using the performance of a session N and the score of the vigilance factor. As the vigilance factor measures the tendency to trust others' motives and intentions, it might be interesting to provide deeper technical explanations to subjects with a high vigilance score, as they could be skeptical.
>However, our analyses suggest that the users' traits alone may not be enough to predict their MT-BCI performances across experiments, at least with data collected from a small number (here 3 or less) of sessions. This is a negative result, as it may suggest that users' traits may not have such a strong contribution to BCI performance variations.