BIT BUDGET: HOW MUCH INFORMATION DOES EEG ADD BEYOND A FROZEN LANGUAGE PRIOR?

EEG-to-text systems can produce fluent language even when the underlying EEG signal is corrupted, raising a fundamental question: how much text-specific information does the neural signal contribute beyond a language prior? We introduce Bit Budget, a calibrated framework that treats neural-to-language decoding as a communication-channel measurement problem and quantifies evidence dependent information beyond a frozen language prior. On the ZuCo corpus, gaze provides a significant information lower bound of 0.107 bits/token, while the evaluated EEG representation yields 0.000 bits/token with no significant evidence dependence. On the independent COFETT corpus, the same framework recovers an injected text-dependent signal of 0.149 bits/token, whereas real EEG remains within ±0.004 bits/token across the evaluated conditions. Notably, EEG still decodes four-way task/session identity with 82.1% balanced accuracy, indicating that the representation contains structured information that does not translate into detectable token-level linguistic evidence. These findings expose a gap between language-generation performance and dependence on neural evidence, and motivate calibrated information measures and explicit evidence-destruction controls as complementary tools for evaluating neural-to-language systems.