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

arXiv:2604.16370v1 Announce Type: cross Abstract: Decoding natural language from non-invasive electroencephalography (EEG) remains fundamentally limited by low signal-to-noise ratio and restricted information bandwidth. This raises a fundamental question regarding whether sentence-level linguistic structure can be reliably recovered from such signals. In this work, we suggest that this assumption may not hold under realistic information constraints, and instead propose a semantic compression hyp