Determining Levels of Affective States with Riemannian Geometry Applied to EEG Signals

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.