Eigenvalue problems occupy a central role in Riemannian geometry, providing profound insights into the interplay between geometry and analysis. At their core, these problems involve the study of ...

Abstract: Common Spatial Pattern (CSP) is a popular feature extraction algorithm used for electroencephalogram (EEG) data classification in brain-computer interfaces. One of the critical operations ...

Riemannian geometry offers an elegant mathematical framework for the analysis of data that naturally resides on curved spaces, particularly the manifold of symmetric positive definite (SPD) matrices.

Abstract: Accurate classification of cognitive states from Electroencephalographic (EEG) signals is crucial in neuroscience applications such as Brain-Computer Interfaces (BCIs). Classification ...

Bringing together a Riemannian geometry account of visual space with a complementary account of human movement synergies we present a neurally-feasible computational formulation of visuomotor task ...

This repository contains the official implementation of the paper Score-Based Pullback Riemannian Geometry by Willem Diepeveen*, Georgios Batzolis*, Zakhar Shumaylov, and Carola-Bibiane Schönlieb. In ...

Lightlike warped product manifolds are considered in this paper. The geometry of lightlike submanifolds is difficult to study since the normal vector bundle intersects with the tangent bundle. Due to ...

The Riemannian Manifold module is a core component of the Integrative Historical Prediction (IHP) framework. It provides a geometric representation of societal states and dynamics, allowing for ...