부산대학교 도서관

Personalized medical brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Critically, these devices require accurate, energy-efficient brain-state classifiers to determine the precise moment when the treatment neuromodulation efficacy is maximized, such as before the onset of a seizure in epilepsy [1]. The SoC presented in this work addresses this requirement by combining a bank of 8 neural signal ADCs with BrainForest, an accurate, low-power classification core comprised of a 1024-tree exponentially decaying memory decision forest (EDM-DF). Full closed-loop neuromodulation is supported through the responsive actuation of an on-chip electrical neurostimulator.