Neuro-KE: Knowledge-Guided Interfaces for Semantically Grounded EEG Foundation Models

Foundation Models for Electroencephalography (EEG) are trained with masked reconstruction, contrastive alignment, and language-model interfaces, yet their learned representations remain difficult to inspect: raw EEG lacks canonical token semantics, and models may preserve waveform detail while discarding physiologically meaningful structure. We introduce Neuro-KE (Neuro-Knowledge Engine), a knowledge interface that computes EEG descriptors without additional human annotation and renders them as paradigm-specific supervision for semantic grounding. Rather than using handcrafted features as standalone classifiers, Neuro-KE converts the same domain knowledge into numeric prediction targets, feature-state text anchors, and feature-grounded instruction answers. Across three interfaces, Neuro-KE improves aggregate grounding and transfer metrics, with task-dependent exceptions: it improves balanced accuracy (BAcc) by 2.60 points over reconstruction-only masked pretraining across 12 datasets and three backbones, improves six-dataset EEG–text validation BAcc by 2.23 points over label-only anchors, and improves EEG-MLLM feature-grounded generation to ROUGE-L F1 0.607 and BERTScore F1 0.929. These results suggest that established EEG descriptors can serve as reusable grounding signals for inspecting and supervising EEG foundation-model interfaces.