Brain-CLIPLM: Decoding Compressed Semantic Representations in EEG for Language Reconstruction

This work proposes a semantic compression hypothesis to overcome limitations in EEG-to-text decoding, suggesting that EEG signals encode compressed semantic anchors rather than full linguistic structure. It introduces Brain-CLIPLM, a two-stage framework for semantic anchor extraction via contrastive learning and sentence reconstruction using a retrieval-grounded large language model.