Teaching Neural Decoders with Language

Brain–computer interfaces decode neural activity, yet class-label supervision (CLS) leaves relationships among classes implicit. We propose Language as Supervision (LaS), which aligns neural features with natural-language class descriptions through learned projections and predicts classes using candidate text prototypes. We evaluate seven encoder architectures with independently trained models for visual, motor, and speech decoding from scalp and intracranial recordings. LaS improves speech-decoding accuracy by up to 17.15 pp over matched CLS baselines and supports subject and session adaptation. Frequency-structured neural–text correspondence further enables conditional decoding of unseen stimulus frequencies in steady-state visual evoked potential (SSVEP) responses. Dual-sided attribution visualization shows contributions from neural signal components and prompt words to the same class-specific matching score. These findings suggest that text descriptions can serve as general-purpose references for learning and interpretation in neural decoding.