ELASTIQ: EEG-Language Alignment with Semantic Task Instruction and Querying
This addresses a bottleneck in brain-computer interfaces by enabling semantic task guidance for EEG decoding, though it builds incrementally on existing EEG foundation models.
The paper tackles the challenge of incorporating language instructions as prior constraints for EEG representation learning by introducing ELASTIQ, a foundation model for EEG-language alignment that achieves state-of-the-art performance on 14 out of 20 datasets across five task categories.
Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interfaces (BCI). However, existing approaches still struggle to incorporate language instructions as prior constraints for EEG representation learning, limiting their ability to leverage the semantic knowledge inherent in language to unify different labels and tasks. To address this challenge, we present ELASTIQ, a foundation model for EEG-Language Alignment with Semantic Task Instruction and Querying. ELASTIQ integrates task-aware semantic guidance to produce structured and linguistically aligned EEG embeddings, thereby enhancing decoding robustness and transferability. In the pretraining stage, we introduce a joint Spectral-Temporal Reconstruction (STR) module, which combines frequency masking as a global spectral perturbation with two complementary temporal objectives: random masking to capture contextual dependencies and causal masking to model sequential dynamics. In the instruction tuning stage, we propose the Instruction-conditioned Q-Former (IQF), a query-based cross-attention transformer that injects instruction embeddings into EEG tokens and aligns them with textual label embeddings through learnable queries. We evaluate ELASTIQ on 20 datasets spanning motor imagery, emotion recognition, steady-state visual evoked potentials, covert speech, and healthcare tasks. ELASTIQ achieves state-of-the-art performance on 14 of the 20 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. The code and pre-trained weights will be released.