Continuous-Latent Predictive Modeling with Semantic Alignment for EEG-Language Foundation Models
Recent advances in EEG foundation models have demonstrated the potential of large-scale pretraining to enable generalizable neural decoding across subjects, recording environments, and datasets. However, dominant pretraining paradigms face key challenges: masked autoencoding tends to prioritize low-level signal reconstruction over task-relevant semantics, while autoregressive modeling creates a mismatch between continuous neural dynamics and discrete token spaces. To address these challenges, new strategies are needed to effectively align continuous EEG representations with natural-language semantics and enable their integration with large language models. Accordingly, we propose Brain La...
| Authors | Myeong-Ju Cho, Hye-Bin Shin, Seo-Hyun Lee et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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