Ewen: A Covariate-Aware Foundation Model for EEG Understanding

EEG foundation models have improved transferable neural representation learning, but existing EFM–LLM frameworks still largely use language models as closed-set text classifiers. We propose Ewen, a covariate-aware EEG foundation model that incorporates physiological context into EEG representations for decoding and natural-language description. Ewen converts EEG and available auxiliary physiological signals into descriptorized covariates, providing language-aligned context for interpreting neural signals. These covariates are integrated with EEG tokens through covariate-aware token geometry, connecting physiological context with the temporal and channel structure of EEG representations. The language backbone is then adapted through language-reference-guided gradient projection to control language drift during task learning. Experiments across six EEG benchmarks demonstrate improved closed-vocabulary decoding, while generation evaluations show that Ewen can describe spectral characteristics, physiological relationships, and temporal and spatial variations. Together, these results support the value of physiological context for EEG modeling and represent a step toward physiologically grounded EEG understanding.