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Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) make use of brain signals produced during mental imagery tasks to control a computerised system. The current unreliability of MI-BCIs could be due, at least in part, to the use of inappropriate usertraining 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, this paper aims at creating a statistical model that could explain/predict the performances 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. Our first results showed that using the users’ traits only may enable the prediction of performances within one multiple-session experiment, but might not be sufficient to reliably predict MI-BCI performances across experiments.

Are users' traits informative enough to predict/explain their mental-imagery based BCI performances?