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Motor imagery-based brain–computer interfaces (MI-BCIs) rely on interactions between humans and machines. The (learning) characteristics of both components are key to understand and improve performances. Data-driven methods are often used to select/extract features with very little neurophysiological prior. Should they include prior knowledge and which one? We study the relationship between MI-BCI performances and characteristics of a commonly used subject-specific Most Discriminant Frequency Band (MDFB). Our results showed a correlation between the selected MDFB characteristics (mean and width) and performances. While a causal link could not be determined, online MI-BCI performances obtained using a constrained algorithm --enforcing characteristics associated to high performances– seemed higher than with an unconstrained algorithm. Finally, we used machine learning to 1) predict MI-BCI performances from MDFB characteristics and 2) select automatically the optimal algorithm. This way, the constrained algorithm could improve performances for subjects with either clearly distinct or no distinct EEG patterns.

When should MI-BCI feature optimization include prior knowledge, and which one?