Disambiguating Semantic Units for EEG-to-Text Reconstruction

EEG-to-text decoding aims to recover linguistic semantics from non-invasive neural signals. However, reliable decoding requires not only fluent generation, but also identifiable semantic evidence and its compositional structure. We reveal a previously underexplored failure mode in fine-grained EEG semantic decoding: semantic ambiguity arises at two distinct scales. Within a sample, shared sentence context dominates query-specific information, causing different semantic queries to converge toward similar representations; across samples, highly central semantic candidates repeatedly attract unrelated EEG queries, inducing systematic overmatching. These observations suggest that reliable semantic decoding requires jointly resolving context-induced convergence and hubness before composing the recovered evidence into language. Based on this finding, we propose Neural conteXtual Unified Semantics (NEXUS), a unified framework that disentangles shared contextual semantics from query-specific evidence, calibrates biased semantic matching from both candidate and query perspectives, and recomposes the recovered semantic units using delexicalized structural priors. Experiments on heterogeneous EEG datasets spanning natural reading and imagined across languages speech demonstrate that NEXUS substantially reduces within-sample representation convergence and cross-sample overmatching, leading to more discriminative semantic-unit identification and more faithful EEG-to-text reconstruction.