University of Groningen

The study tested seven detection methods to assess whether motor imagery BCIs can identify movement classes absent from training rather than force them into known categories. For many users, model uncertainty was higher for familiar classes than for unfamiliar ones, undermining methods that work well in other machine learning domains. Detection performance was correlated with performance on the trained task. Deep Ensemble and Monte Carlo-Dropout models achieved an on-task AUC of 0.9 and out-of-distribution detection ability up to 0.7. The authors conclude that rejecting unfamiliar cognitive states is feasible for models and participants with high task performance.