Differentiating resting brain states using ordinal symbolic analysis

Abstract

Symbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination. Here, we use ordinal analysis to investigate the transition between eyes closed (EC) and eyes open (EO) resting states. We analyze two electroencephalography datasets (with 71 and 109 healthy subjects) with different recording conditions (sampling rates and the number of electrodes in the scalp). Using as diagnostic tools the permutation entropy, the entropy computed from symbolic transition probabilities, and an asymmetry coefficient (that measures the asymmetry of the likelihood of the transitions between symbols), we show that the ordinal analysis applied to the raw data distinguishes the two brain states. In both datasets, we find that, during the EC-EO transition, the EO state is characterized by higher entropies and lower asymmetry coefficient, as compared to the EC state. Our results thus show that these diagnostic tools have the potential for detecting and characterizing changes in time-evolving brain states. In the “big data” era, many efforts are being devoted to extracting useful information from complex signals. The human brain is one of the most complex systems that one can try to understand. In the last few decades, the development and popularization of recording techniques, such as electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI), have provided the scientific community with a huge amount of data: different types of brain signals, recorded with different spatio-temporal resolution, under different behavioral or cognitive states, from healthy or from dysfunctioning subjects. The underlying brain states are, in spite of many efforts, still poorly understood. Here, we use a symbolic analysis tool to investigate EEG signals recorded from healthy subjects during a simple behavioral task: the subjects remain in the resting state with eyes closed (EC state) during an interval of time and then open their eyes (EO state). We show that symbolic analysis applied to the raw EEG signals detects the transition and identifies subtle differences between the EC and EO brain states.