Simple Probabilistic Data-driven Model for Adaptive BCI Feedback

Overview & Synopsis

Due to abundant signal and user variability among others, BCIs remain difficult to control. To increase performance, adaptive methods are a necessary means to deal with such a vast spectrum of variable data. Typically, adaptive methods deal with the signal or classification corrections (adaptive spatial filters [1], co-adaptive calibration [2], adaptive classifiers [3]). As such, they do not necessarily account for the implicit alterations they perform on the feedback (in real-time), and in turn, on the user, creating yet another potential source of unpredictable variability. Namely, certain user's personality traits and states have shown to correlate with BCI performance, while feedback can impact user states [4]. For instance, altered (biased) feedback was distorting the participants' perception over their performance, influencing their feeling of control, and online performance [5]. Thus, one can assume that through feedback we might implicitly guide the user towards a desired state beneficial for BCI performance. We propose a novel, simple probabilistic, data-driven dynamic model to provide such feedback that will maximize performance.