Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech
Imagined speech decoding from EEG offers a promising route toward naturalistic brain-computer interfaces. Traditional closed-set classifiers treat semantic categories as independent labels and do not explicitly exploit relationships among linguistic intents, limiting output flexibility. We adapt a multimodal contrastive learning framework to imagined speech, aligning EEG trials with language representations in the embedding space of a frozen pretrained text encoder. An EEG encoder is trained with a composite objective combining semantically reweighted EEG-sentence alignment and text-prototype-based class discrimination, encouraging semantic structure while preserving separability among 39...
| Authors | Shahryar G. Namdari, Francesco Iacomi, Riccardo Barbieri et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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