SemLoop: Closing the Loop Between Semantic Relevance and EEG Foundation Model Utility

EEG foundation models (EEG FMs) learn transferable representations from heterogeneous neural data, yet adapting them to task-specific patterns remains challenging. Language offers a natural interface for incorporating task knowledge, but task-relevant semantics can vary substantially in their utility to the target model. Existing approaches largely rely on predefined semantics, leaving a gap between semantic relevance and model utility. We propose SemLoop, a closed-loop semantic adaptation framework that bridges this gap through model feedback. SemLoop comprises three mechanisms: EEG-Grounded Semantic Alignment (EGSA) grounds language-derived neurophysiological concepts in EEG representations; Model-Validated Semantic Refinement (MVSR) uses LLM-based evaluator and planner agents to iteratively refine semantics with target-model feedback; and Experience-Internalized Semantic Planning (EISP) internalizes successful refinements for subsequent planning. Across nine EEG benchmarks and multiple EEG FMs, SemLoop consistently outperforms full fine-tuning and predefined semantic-guided approaches. Further analyses reveal heterogeneous utility among task-relevant semantics and show that model-validated refinement shifts them toward higher model utility. The code is available at https://anonymous.4open.science/r/SemLoop-75F0.