Early brain-computer interface (BCI) systems were mainly based on prior neurophysiological knowledge coupled with feedback training, while state-of-the-art interfaces rely on data-driven, machine learning (ML)-oriented methods. Despite the advances in BCI that ML can be credited with, the performance of BCI solutions is still not up to the mark, posing a major barrier to the widespread use of this technology. This paper proposes a novel, automatic feature selection method for BCI able to leverage both data-dependent and expert knowledge to suppress noisy features and highlight the most relevant ones thanks to a fuzzy logic (FL) system. Our approach exploits the capability of FL to increase the reliability of decision-making by fusing heterogeneous information channels while maintaining transparency and simplicity. We show that our method leads to significant improvement in classification accuracy, feature stability and class bias when applied to large motor imagery or attempt datasets including end-users with motor disabilities. We postulate that combining data-driven methods with knowledge derived from neuroscience literature through FL can enhance the performance, explainability, and learnability of BCIs.