LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces

Electroencephalography (EEG) foundation models learn transferable representations for brain–computer interfaces, but existing approaches treat tasks and labels as discrete targets and fail to use the semantic structure of natural-language task instructions to guide representation learning. We present LEAF, a foundation model for EEG–Language Alignment with Semantic Task Instruction and Querying. LEAF integrates task-aware semantic guidance to produce structured and linguistically aligned EEG embeddings, thereby enhancing decoding robustness and transferability. In the EEG pretraining stage, we introduce a joint Spectral–Temporal Reconstruction (STR) framework that captures the coupled spectral rhythms and temporal dynamics of EEG signals. STR applies randomized spectral perturbation to enhance frequency robustness and uses two complementary temporal objectives to learn both contextual and sequential structure. In the EEG-Language alignment stage, we propose the Instruction-conditioned Q-Former (IQF). This query-based cross-attention transformer injects instruction embeddings into EEG tokens and achieves semantic alignment with textual label embeddings through learnable queries. We evaluate LEAF on 16 downstream datasets spanning motor imagery, emotion recognition, steady-state visual evoked potentials, covert speech, and healthcare tasks. LEAF achieves state-of-the-art performance on 12 of the 16 datasets and obtains the best average results across all five task categories. Importantly, our analyses reveal for the first time that explicit task instructions serve as semantic priors guiding EEG embeddings into coherent and linguistically grounded spaces. Code is available at https://anonymous.4open.science/r/LEAF-Model