Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
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. To this end, we propose FUSED, a Foundation-guided Source-free EEG Decoding framework that integrates a large-scale FM with a compact Specialist Model (SM) via dual-br...
| Authors | Peiliang Gong, Han Zhang, Zhen Jiang et al. |
|---|---|
| Primary category | eess.SP |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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