BCIwiki (bciwiki.com) — This preprint was published on bioRxiv on July 15, 2026, and has not been peer-reviewed. Brain-computer interfaces have achieved impressive performance by decoding motor and articulatory signals associated with speech production. However, considerably less is known about whether higher-level semantic representations can be decoded from human cortical activity.
Researchers Smith C., Inchyna S., Barrentine B., and Nelson M. J. from the University of Alabama at Birmingham recorded intracranial neural activity from patients undergoing stereotactic electroencephalography (sEEG) for clinical epilepsy monitoring while they performed language tasks requiring semantic processing. High-gamma power was extracted from local field potentials and used to generate trial-level features for supervised machine-learning classification.
Semantic category information was decoded significantly above chance, with mean classification accuracy reaching 29.8% across 15 semantic categories (chance = 6.7%). These findings demonstrate that high-gamma activity contains information about conceptual category membership that can be extracted on individual trials. The results provide evidence that semantic information is accessible from intracranial population recordings and support the feasibility of semantic decoding as a complementary direction for future language BCIs.