Who Did What to Whom? Relational Structure in EEG Semantic Decoding

Recovering structured meaning from neural activity is a central goal of brain-to-language decoding and a key step toward natural communication through brain-computer interfaces. Recent semantic-unit decoding frameworks provide an interpretable route to recovering semantic content from EEG, yet content alone does not specify its relational organization. This leaves a fundamental boundary between recovering what is represented and identifying who did what to whom. To study this boundary, we develop a three-stage evaluation framework that separates role-neutral semantic content from relational organization. (1) For functional relevance, text-side content-controlled structural interventions show that correct predicate-argument binding improves fidelity to the intended proposition beyond semantic content alone. (2) For structural readout, contextual representations support reproducible role-related readout beyond retrieved semantic-unit identity, while larger relation-predictability effects are also reproduced by non-neural controls, limiting their interpretation as EEG-specific structure. (3) For occurrence-specific attribution, EEG conditioning yields small gains beyond semantic content, but matched-record specificity remains weak and heterogeneous. Together, these results identify relational structure as a distinct target for EEG semantic decoding and provide a principled basis for separating functional relevance, structural readout, and occurrence-specific neural attribution.