Using Riemannian geometry for SSVEP-based Brain Computer Interface

14 Jan 2015Emmanuel K. KalungaSylvain ChevallierQuentin Barthelemy

Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constructing a representation which is invariant to these perturbations... (read more)

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