AI RESEARCH
Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
arXiv CS.LG
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ArXi:2605.00857v1 Announce Type: cross Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA methods rely solely on the limited internal knowledge of source-pretrained models, leading to inferior cross-domain generalization and unreliable pseudo-labels. Although EEG Foundation Models (FMs) pretrained on large-scale data exhibit strong generalizability, their potential in SFDA remains largely unexplored.