  Functional connectivity is a key approach to investigate oscillatory
activities of the brain that provides important insights on the underlying
dynamic of neuronal interactions and that is mostly applied for brain activity
analysis. Building on the advances in information geometry for brain-computer
interface, we propose a novel framework that combines functional connectivity
estimators and covariance-based pipelines to classify mental states, such as
motor imagery. 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, called FUCONE, is evaluated on different conditions and
datasets. Using a meta-analysis to aggregate results across datasets, FUCONE
performed significantly better than all state-of-the-art methods. 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.
