Preprint: OSPDIM Online Source-Free Adaptation for Class-Imbalanced EEG

Summary A preprint introduces OSPDIM, a source-free online unsupervised domain adaptation framework for class-imbalanced label shift in online EEG brain-computer interfaces. The study argues that Riemannian alignment methods such as the Riemannian centering transform handle covariate shift but implicitly assume balanced class priors, so shifting label distributions in real online use geometrically misalign the target data; OSPDIM adds a manifold-constrained bias parameter in the Riemannian tangent-space mapping, optimized by information maximization, and estimates and corrects the geometric bias online rather than relying on global batch statistics. 2D SPD matrix simulations showed OSPDIM correcting misalignment that standard centering fails on, and across multiple motor imagery datasets it significantly outperformed standard Riemannian baselines, especially online. The study is a preprint and has not been peer-reviewed.