Objective: Relying on the idea that functional connectivity provides important insights on the underlying dynamic of neuronal interactions, we propose a novel framework that combines functional connectivity estimators and covariance-based pipelines to improve the classification of mental states, such as motor imagery. Methods: A Riemannian classifier is trained for each estimator and an ensemble classifier combines the decisions in each feature space. A thorough assessment of the functional connectivity estimators is provided and the best performing pipeline among those tested, called FUCONE, is evaluated on different conditions and datasets. Results: Using a meta-analysis to aggregate results across datasets, FUCONE performed significantly better than all state-of-the-art methods. Conclusion: The performance gain is mostly imputable to the improved diversity of the feature spaces, increasing the robustness of the ensemble classifier with respect to the inter-and intra-subject variability. Significance: Our results offer new insights into the need to consider functional connectivity-based methods to improve the BCI performance.