Emotion recognition from electroencephalography (EEG) often relies on Euclidean features that ignore the curved geometry of covariance matrices. We introduce a Riemannian-manifold pipeline which, combined with the Fisher Geodesic Minimum Distance to Mean (FgMDM) classifier, leverages the full geometry of symmetric positive-definite (SPD) EEG covariances. The approach applies an additional geodesic-mean contraction that stabilizes trial covariances before tangent space projection. Experiments on the five-class SEED-V dataset show high accuracy, robustness to session-to-session variability and improved interpretability relative to baselines. These results highlight Riemannian geometry as a powerful framework for emotion recognition with high-dimensional, non-stationary EEG.
Wosiak et al. (Wed,) studied this question.