  Parkinson's Disease afflicts millions of individuals globally. Emerging as a
promising brain rehabilitation therapy for Parkinson's Disease, Closed-loop
Deep Brain Stimulation (CL-DBS) aims to alleviate motor symptoms. The CL-DBS
system comprises an implanted battery-powered medical device in the chest that
sends stimulation signals to the brains of patients. These electrical
stimulation signals are delivered to targeted brain regions via electrodes,
with the magnitude of stimuli adjustable. However, current CL-DBS systems
utilize energy-inefficient approaches, including reinforcement learning, fuzzy
interface, and field-programmable gate array (FPGA), among others. These
approaches make the traditional CL-DBS system impractical for implanted and
wearable medical devices. This research proposes a novel neuromorphic approach
that builds upon Leaky Integrate and Fire neuron (LIF) controllers to adjust
the magnitude of DBS electric signals according to the various severities of PD
patients. Our neuromorphic controllers, on-off LIF controller, and dual LIF
controller, successfully reduced the power consumption of CL-DBS systems by 19%
and 56%, respectively. Meanwhile, the suppression efficiency increased by 4.7%
and 6.77%. Additionally, to address the data scarcity of Parkinson's Disease
symptoms, we built Parkinson's Disease datasets that include the raw neural
activities from the subthalamic nucleus at beta oscillations, which are typical
physiological biomarkers for Parkinson's Disease.
