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In this study we focus on the diagnosis of Parkinson's Disease (PD) based onelectroencephalogram (EEG) signals. We propose a new approach inspired by thefunctioning of the brain that uses the dynamics, frequency and temporal contentof EEGs to extract new demarcating features of the disease. The method wasevaluated on a publicly available dataset containing EEG signals recordedduring a 3-oddball auditory task involving N = 50 subjects, of whom 25 sufferfrom PD. By extracting two features, and separating them with a straight lineusing a Linear Discriminant Analysis (LDA) classifier, we can separate thehealthy from the unhealthy subjects with an accuracy of 90 % $(p < 0.03)$ usinga single channel. By aggregating the information from three channels and makingthem vote, we obtain an accuracy of 94 %, a sensitivity of 96 % and aspecificity of 92 %. The evaluation was carried out using a nestedLeave-One-Out cross-validation procedure, thus preventing data leakage problemsand giving a less biased evaluation. Several tests were carried out to assessthe validity and robustness of our approach, including the test where we useonly half the available data for training. Under this constraint, the modelachieves an accuracy of 83.8 %.

Sparse Dynamical Features generation, application to Parkinson's Disease diagnosis