Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease
Fig 6
(a) Bayesian ADC control diagram. The Bayesian ADC’s inner loop was composed of a phase/power based feedback stimulator. The outer Bayesian optimization loop was composed of a Gaussian process (GP), and acquisition function. The Gaussian process builds a model of how the stimulation parameters affect the feedback signal, and the acquisition function uses this information to select the next parameter set. (b) Overview of the Bayesian ADC’s cyclic operation. The Bayesian ADC sets the stimulator parameters and applies phase/power based stimulation to the BGTCS for 20s. It then estimates the effect of those parameters on beta power, and updates its GP with the new observation. Finally, it optimizes its acquisition function, and selects the next parameter set.