GeMoT: Encoding Class Knowledge in Geometry and Moments for Source-Free EEG Domain Adaptation

Source-free domain adaptation has garnered increasing attention for cross-subject electroencephalography (EEG) decoding, as it eliminates the need for source-data sharing and labeled target calibration. However, strong source-domain performance does not guarantee transferability, and inter-subject variability can undermine source-model predictions. We propose a Geometry and Moment Transfer (GeMoT) framework that jointly encodes source class knowledge in classifier geometry and feature moments to improve source-model transferability and guide target-domain adaptation. For source-domain model training, we introduce a neural-collapse-inspired rule that replaces class-specific classifier weights with pooled source class prototypes, providing a structured initialization for transfer. Using these prototypes, we construct a memory bank of cross-channel means and variances to preserve complementary class statistics. Target features compensated by these feature moments are matched to corresponding classifier weights via cosine similarity to generate pseudo-labels. We design a neighbourhood attraction loss for local prediction consistency, a progressively weighted compensation-guided supervision loss to train the target-domain model with these pseudo-labels, and an inter-class dispersal loss to reduce prediction overlap across pseudo-classes. The adaptation process also incorporates a label-free early-stopping criterion based on the normalized entropy of the pseudo-label class distribution. Offline leave-one-subject-out evaluation covers three backbones and three EEG datasets spanning P300 detection, motor imagery, and vigilance monitoring. With one fixed backbone per dataset, mean accuracies reach 69.36%, 72.50%, and 82.45% on P300, BNCI2014002, and SADT, respectively. These results demonstrate that coupling classifier geometry with class-specific statistical memory provides a robust, generalizable paradigm for source-free cross-subject EEG decoding.