Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson’s disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13–30 Hz) activity as a feedback signal, offers the potential to adapt DBS automatically in response to changes in patient symptoms and side effects. However, clinically implemented closed-loop techniques have been limited to date to simple control algorithms, due to the inherent uncertainties in the dynamics involved. Model-free control, which has already seen successful applications in the field of bioengineering, offers a way to avoid this limitation and provides an alternative method to apply modern control approach to selective suppression of pathological oscillations. In this paper, we use a computational mean-field model of parkinsonian brain activity to show that model-free control provides selective disruption of pathological beta activity within the network. We show that this technique successfully detects and suppresses the beta activity, while preserving the non-pathological activity in the gamma ( \(\ge 30\) Hz) frequency band, even in the presence of extraneous noise. These results demonstrate the potential for MFC as a viable candidate for closed-loop DBS algorithm in the treatment of Parkinson’s disease.