  In this study we focus on the diagnosis of Parkinson's Disease (PD) based on
electroencephalogram (EEG) signals. We propose a new approach inspired by the
functioning of the brain that uses the dynamics, frequency and temporal content
of EEGs to extract new demarcating features of the disease. The method was
evaluated on a publicly available dataset containing EEG signals recorded
during a 3-oddball auditory task involving N = 50 subjects, of whom 25 suffer
from PD. By extracting two features, and separating them with a straight line
using a Linear Discriminant Analysis (LDA) classifier, we can separate the
healthy from the unhealthy subjects with an accuracy of 90 % $(p < 0.03)$ using
a single channel. By aggregating the information from three channels and making
them vote, we obtain an accuracy of 94 %, a sensitivity of 96 % and a
specificity of 92 %. The evaluation was carried out using a nested
Leave-One-Out cross-validation procedure, thus preventing data leakage problems
and giving a less biased evaluation. Several tests were carried out to assess
the validity and robustness of our approach, including the test where we use
only half the available data for training. Under this constraint, the model
achieves an accuracy of 83.8 %.
