  Recordings of electrical brain activity carry information about a person's
cognitive health. For recording EEG signals, a very common setting is for a
subject to be at rest with its eyes closed. Analysis of these recordings often
involve a dimensionality reduction step in which electrodes are grouped into 10
or more regions (depending on the number of electrodes available). Then an
average over each group is taken which serves as a feature in subsequent
evaluation. Currently, the most prominent features used in clinical practice
are based on spectral power densities. In our work we consider a simplified
grouping of electrodes into two regions only. In addition to spectral features
we introduce a secondary, non-redundant view on brain activity through the lens
of Tsallis Entropy $S_{q=2}$. We further take EEG measurements not only in an
eyes closed (ec) but also in an eyes open (eo) state. For our cohort of healthy
controls (HC) and individuals suffering from Parkinson's disease (PD), the
question we are asking is the following: How well can one discriminate between
HC and PD within this simplified, binary grouping? This question is motivated
by the commercial availability of inexpensive and easy to use portable EEG
devices. If enough information is retained in this binary grouping, then such
simple devices could potentially be used as personal monitoring tools, as
standard screening tools by general practitioners or as digital biomarkers for
easy long term monitoring during neurological studies.
