A quality control analysis of the resting state hypothesis via permutation entropy on EEG recordings

TL;DR

This paper introduces a permutation entropy-based method for assessing the stability of resting state EEG recordings, enabling quality control and revealing age-related differences in brain activity stability.

Contribution

It presents a novel, statistically robust approach to evaluate resting state stability using permutation entropy and its uncertainty estimation from single EEG time series.

Findings

Higher instability in elderly EEG data

Method reliably assesses resting state stationarity

Potential for real-time application

Abstract

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.