Extending Riemannian Brain-Computer Interface to Functional Connectivity Estimators
Abstract
This abstract describes a novel approach for handling brain-computer interfaces (BCI), that could be used for robotic applications. State-of-the-art approaches rely on the classification of covariance matrices in the manifold of symmetric positive-definite matrices. Functional connectivity estimators have demonstrated their reliability and are good candidates to improve the classification accuracy of covariance-based methods. This abstract explores possible application of functional connectivity in Riemannian BCI.
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