A Real-Time On-Demand Deep Brain Stimulation Device Design and Validation

In this paper, an on-demand Deep brain stimulation (DBS) device is designed to deliver stimulation current pulses according to beta band power of local field potentials (LFPs). The LFPs are collected by signal processing module and analyzed in digital signal processor (DSP) with the Fast Fourier transform method to calculate the power in the beta band. Then, it controls the stimulation pulses by monitoring the beta power and comparing with a predefined threshold. And based on the neural mass model, a self-oscillation network composed of two neural populations, the real LFPs from basal ganglia are simulated by using DSP and step-down circuits. In addition, an on-demand deep brain stimulation test platform that supports real-time model simulation and close-to-real application testing was built to validate the DBS device. Validation results indicate that the device can control the On-Off state of the stimulation accurately. This on-demand control method can prevent the overstimulation of the brain and extend the battery lifespan of the stimulation device.