Parkinson’s disease is a chronic and progressive nervous system disorder that affects movement. To improve the quality of life of patients, medication and treatments have been developed, among which SubThalamic Nucleus Deep Brain Stimulation (STN-DBS) is thought to be the most effective treatment. STN-DBS consists of the surgical implantation of microelectrodes in the basal ganglia and the delivery of an electric stimulation. However, the effectiveness of STN-DBS is dependent on the accuracy of the implantation. For this purpose, microelectrode recordings are obtained during the surgery to better identify the target location. In this work, we devise a simple yet effective machine learning method that, given microelectrode recordings, enables the detection of the SubThalamic Nucleus with high accuracy and explainability.