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We use Electroencephalography (EEG) data to detect early stage Parkinson’s Disease. Firstly, we present a novel representation for EEG data, a 7-variate series of band power coefficients, which enables the use of (previously inaccessible) time series classification methods. Our approach achieves over 90% accuracy, recall and precision which rivals state of the art methods. This is particularly impressive given the early stage Parkinson’s Disease participants and limited optimisation of the model thus far. Secondly we present a framework for determining the importance of individual brain regions in Parkinson’s Disease classification. We find that across different EEG data types, it is the Prefrontal brain region that has the most predictive power for the presence of Parkinson’s Disease