Altindis, FatihYilmaz, BulentCongedo, Marco2025-09-252025-09-2520249798350369106https://doi.org/10.1109/DASA63652.2024.10836450https://hdl.handle.net/20.500.12573/4173This paper presents many-to-many domain adaptation strategy, named group learning, for motor imagery brain-computer interfaces (BCIs). Group learning, grounded in Riemannian geometry, simultaneously aligns multiple domains in a unified model, whereas fast alignment approach integrates new, unseen domains without re-estimating alignment matrices for all domains. Group learning creates a single machine learning model using data from previous subjects and/or sessions. Fast alignment utilizes the already trained model for an unseen domain without requiring any additional classifier training. The tests on five publicly available motor imagery databases demonstrate the robustness of group learning against negative learning. The classification accuracy scores of binary and multiclass databases show comparable, if not superior, performance to conventional subject-wise learning method. © 2025 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/openAccessBrain-Computer Interface (Bci)Domain AdaptationMotor ImageryRiemannian GeometryTransfer LearningAdversarial Machine LearningBrain Computer InterfaceContrastive LearningImage ClassificationTransfer LearningAdaptation StrategiesBrain-Computer InterfaceDomain AdaptationFast AlignmentsGroup LearningMany to ManyMotor ImageryMultiple DomainsRiemannian GeometryFederated LearningConference Object10.1109/DASA63652.2024.108364502-s2.0-85217269243