Machine Learning for Deep Brain Stimulation Efficacy using Dense Array EEG

Deep brain stimulation (DBS) is well recognized as an effective treatment for symptoms of movement disorders such as Parkinson's disease (PD), Essential Tremor, and dystonia. The selection of the appropriate contact on the DBS lead for optimal clinical efficacy can be challenging, particularly when considering directional leads. Electroencephalograms (EEG) and electrocorticography has been utilized to better understand the pathophysiology of PD but a methodology to provide an objective biomarker of effective stimulation has yet to be developed. Using machine learning techniques for feature extraction and classification, we contrast high resolution EEG captured during DBS against its resting state counterpart with the DBS off. We demonstrate, using 16 patients under DBS treatment for movement disorders, EEG's informative capacity to detect both effective DBS and the region undergoing stimulation.