EEG Does Not Spell What You Read: Semantic Neighbourhood Synthesis for Open-Vocabulary EEG-to-Text

Open-vocabulary EEG-to-text asks a model to reproduce, word for word, the sentence a person silently read, given only their scalp EEG. We argue that this target is mis-specified for reading and that the field's metrics have concealed the problem. Verbatim decoding partly succeeds when the task is typing, where every character is time-locked to a motor event, but silent reading offers no such clock, and on our evidence the exact word sequence does not survive the skull, though its meaning does. Indeed, on our benchmark the best BLEU-1 and ROUGE-1 belong to a system whose real-EEG performance never rises above its own information-free controls. We recast the problem as semantic reconstruction and introduce Semantic Neighbourhood Synthesis, which treats a trial as a region of meaning rather than a word sequence. An EEG$\to$CLIP encoder retrieves the nearest training sentences, and a language model writes a new sentence from them. Under a uniform protocol with every baseline retrained from scratch, our method is best on nine of the ten metrics that score meaning, improving 24-way retrieval accuracy by $34\%$ relative, and is the only method whose information-free controls fall to chance. We then ask what limits performance, and the answer is not the decoder. Interventions spanning the front-end, the decoder and the signal itself all shift the score by less than run-to-run noise, while the same readout separates subjects and, above all, sentences, with effects $7.4\times$ and $29\times$ that of the entire representation. On this evidence, reading EEG carries the gist of a sentence and very little of its wording.