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Riemannian geometry has been successfully used in many brain-computerinterface (BCI) classification problems and demonstrated superior performance.In this paper, for the first time, it is applied to BCI regression problems, animportant category of BCI applications. More specifically, we propose a newfeature extraction approach for Electroencephalogram (EEG) based BCI regressionproblems: a spatial filter is first used to increase the signal quality of theEEG trials and also to reduce the dimensionality of the covariance matrices,and then Riemannian tangent space features are extracted. We validate theperformance of the proposed approach in reaction time estimation from EEGsignals measured in a large-scale sustained-attention psychomotor vigilancetask, and show that compared with the traditional powerband features, thetangent space features can reduce the root mean square estimation error by4.30-8.30%, and increase the estimation correlation coefficient by 6.59-11.13%.

EEG-Based User Reaction Time Estimation Using Riemannian Geometry Features