Differentiating resting brain states using ordinal symbolic analysis

TL;DR

This study demonstrates that ordinal symbolic analysis of EEG signals can effectively differentiate between eyes closed and eyes open resting brain states by analyzing entropy and transition asymmetry, revealing distinct neural dynamics.

Contribution

The paper introduces the application of ordinal symbolic analysis to EEG data for distinguishing resting brain states, highlighting its robustness and diagnostic potential.

Findings

EO state has higher entropy than EC state

EO state shows lower transition asymmetry

Ordinal analysis effectively differentiates brain states

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 {EEG} 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 ordinal analysis applied to the raw data distinguishes the two brain states. In both datasets, we find that 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.