Brain-CLIPLM: Semantic Compression for EEG-to-Text Decoding
Decoding natural language from non-invasive electroencephalography (EEG) remains constrained by low signal-to-noise ratio and limited information bandwidth. This raises a central question: can sentence-level language be reliably recovered from such signals? Under realistic information constraints, this direct-recovery assumption may be too strong. We introduce a semantic compression hypothesis: non-invasive EEG may preserve recoverable semantic anchors rather than the full lexical--syntactic form of a sentence. From this perspective, direct sentence reconstruction is overly fine-grained relative to the recoverable information scale of EEG. To address this mismatch, we propose Brain-CLIPLM...
| Authors | Xiaoli Yang, Huiyuan Tian, Yurui Li et al. |
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| Primary category | cs.CL |
| Published | 23 March 2026 |
| Last updated at source | 17 July 2026 |
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