SENSE: Semantic Neural Speech Synthesis from Brain Dynamics via Spatial Graph Encoding

Reconstructing speech from non-invasive brain signals offers a promising pathway for restoring communication in individuals who are cognitively intact but unable to speak. Existing EEG-to-speech approaches formulate this task as \emph{acoustic reconstruction}, optimizing waveform fidelity while ignoring whether the generated speech preserves high-level semantic content. In this work, we revisit this formulation and argue that EEG signals carry not only acoustic but also semantic information. We identify two key limitations of prior methods: (1) the neglect of spatial relationships between EEG electrodes, and (2) the failure to exploit the semantic structure of the N400 paradigm, where congruent and incongruent trials reflect distinct semantic processing. We propose \textbf{SENSE} (\textbf{S}emantic-\textbf{E}EG \textbf{N}eural \textbf{S}peech Synth\textbf{E}sis), which combines a graph-based EEG encoder over electrode geometry with EEG Semantic Conditioning (ESC), aligning EEG to a pretrained semantic space using only congruent trials. On the N400 dataset, SENSE consistently outperforms prior methods on both acoustic and semantic metrics, and model-internal channel attribution suggests distributed reliance on auditory, sensorimotor, and centro-parietal regions, consistent with known speech-perception neuroscience. In the unseen-subject setting, SENSE trained on only two subjects already surpasses the strongest baseline trained on all eighteen subjects in word error rate, and matches it on acoustic metrics with as few as eight subjects.