  The analysis of electrophysiological recordings of the human brain in resting
state is a key experimental technique in neuroscience. Resting state is indeed
the default condition to characterize brain dynamics. Its successful
implementation relies both on the capacity of subjects to comply with the
requirement of staying awake while not performing any cognitive task, and on
the capacity of the experimenter to validate that compliance. Here we propose a
novel approach, based on permutation entropy, to provide a quality control of
the resting state condition by evaluating its stability during a recording. We
combine the calculation of permutation entropy with a method for the estimation
of its uncertainty out of a single time series, thus enabling a statistically
robust assessment of resting state stationarity. The approach is showcased on
electroencephalographic data recorded from young and elderly subjects and
considering both eyes-closed and eyes-opened resting state conditions. Besides
showing the reliability of the approach, the results showed higher instability
in elderly subjects that hint at a qualitative difference between the two age
groups with regard to the distribution of unstable activity within the brain.
The method is therefore a tool that provides insights on the issue of resting
state stability of interest for neuroscience experiments. The method can be
applied to other kinds of electrophysiological data like, for example,
magnetoencephalographic recordings. In addition, provided that suitable
hardware and software processing units are used, its implementation, which
consists here of a posteriori analysis, can be translated into a real time one.
