Stationarity assessment of resting state condition via permutation entropy on EEG recordings

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is 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 assess the reliability of the resting state hypothesis by evaluating its stability during a recording. We combine the calculation of permutation entropy with a method to estimate its uncertainty out of a single time series. The approach is showcased on electroencephalographic data recorded from young and elderly subjects and considering eyes-closed and eyes-opened resting state conditions. Besides highlighting the reliability of the approach, the results show higher instability in elderly subject...