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Differentiating resting brain states using ordinal symbolic analysis.

Carlos Quintero-Quiroz1, Luis Montesano2, Antonio J Pons3

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Summary
This summary is machine-generated.

Ordinal analysis effectively distinguishes brain states like eyes closed (EC) and eyes open (EO) using electroencephalography (EEG) data. This method reveals differences in brain activity complexity and transition patterns between these states.

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Complexity Science

Background:

  • Symbolic analysis methods offer robust tools for analyzing complex time-dependent signals.
  • Ordinal methods, based on value ordering in time series, provide valuable insights even with noisy data.

Purpose of the Study:

  • To investigate the transition between eyes closed (EC) and eyes open (EO) resting states using ordinal analysis.
  • To assess the efficacy of ordinal analysis diagnostics in differentiating brain states from electroencephalography (EEG) data.

Main Methods:

  • Analysis of two EEG datasets from healthy subjects under varying recording conditions.
  • Application of ordinal analysis, including permutation entropy, symbolic transition probabilities, and an asymmetry coefficient.

Main Results:

  • Ordinal analysis successfully distinguished between EC and EO brain states in both datasets.
  • The eyes open (EO) state exhibited higher entropies and lower asymmetry compared to the eyes closed (EC) state during the transition.

Conclusions:

  • Ordinal analysis diagnostic tools can effectively detect and characterize dynamic changes in brain states.
  • These methods hold potential for real-time monitoring and analysis of brain activity transitions.