Directional hand movement can be classified from insular cortex SEEG signals using recurrent neural networks and high-gamma band features.

ABSTRACT: Motor BCIs, with the help of Artificial Intelligence (AI) and machine learning, have shown promise in decoding neural signals for restoring motor function. Structures beyond motor cortex have provided additional sources for movement signals. New evidence points to the role of the insula in motor control, specifically directional hand-movements. In this study, we applied AI and machine learning techniques to decode directional hand-movements from high-gamma band (70-200 Hz) activity in the insular cortex. Seven participants with medication-resistant epilepsy underwent stereo electroencephalographic (SEEG) implantation of depth electrodes for seizure monitoring in the insula. SEEG data were sampled throughout a cued motor task involving three conditions: left-hand movement, right-hand moveme

SUBMITTER: Shao X 

PROVIDER: S-EPMC12357955 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature