Deep brain stimulation (DBS) is an effective treatment for many people living with Parkinson's disease (PD). Although the primary treatment for PD is based on medications, disease progression eventually leads to inadequate symptom control. DBS provides benefits by alleviating motor dysfunctions such as muscle rigidity and tremor. DBS devices deliver electric pulse trains into specific brain regions via implanted electrodes. Existing DBS systems usually provide continuous stimulation with constant settings of parameters such as the amount of charge delivered per pulse. However, PD is characterised by fluctuations in the severity and frequency of impairments. DBS would be improved if stimulation settings were adjusted automatically in response to each patient's ever-changing needs. This requires a device incorporating sensing of signals that estimate the severity of motor impairment linked to an adaptive control algorithm that optimises therapeutic stimulation. Several types of signals are candidates for this function. Spontaneous local field potentials recorded by the DBS electrodes have shown promise in some experimental studies of adaptive DBS. More recently, DBS-evoked potentials have been reported. In particular, evoked resonant neural activity has properties including a larger amplitude than spontaneous potentials, suggesting it may be a suitable feedback signal to control adaptive DBS.