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Ordinal pattern based similarity analysis for EEG recordings.

Gaoxiang Ouyang¹, Chuangyin Dang, Douglas A Richards

¹Department of MEEM, City University of Hong Kong, Kowloon, Hong Kong.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology

|January 26, 2010

Summary

This summary is machine-generated.

This study introduces a new method using ordinal patterns in EEG data to detect brain state changes. The novel dissimilarity measure successfully identified preictal states in absence epilepsy, showing potential for seizure prediction systems.

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EEG Mu Rhythm in Typical and Atypical Development

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Area of Science:

Neuroscience and computational biology.
Time series analysis and signal processing.

Background:

Ordinal pattern analysis, including permutation entropy, is valuable for tracking brain dynamics and detecting changes in EEG data.
Investigating nonlinear dynamical characteristics in EEG is crucial for differentiating brain states.

Purpose of the Study:

To propose a novel dissimilarity measure based on ordinal pattern distributions for analyzing EEG series.
To further explore hidden nonlinear dynamical characteristics in EEG for brain state differentiation.

Main Methods:

EEG series were mapped to phase space to calculate ordinal sequences and their distributions.
A dissimilarity measure was developed using a simple distance metric between ordinal pattern distributions.
A neural mass model simulated EEG data, and the measure was applied to GAERS rat EEG data to distinguish interictal, preictal, and ictal states.

Main Results:

Dissimilarity measures were significantly higher between different brain states compared to within the same state.
The measure successfully detected the preictal state in 64.9% of absence seizures prior to onset.
Analysis of GAERS rat EEG data demonstrated the measure's ability to differentiate brain states.

Conclusions:

The proposed dissimilarity measure effectively reveals dynamic changes in EEG, as observed during epileptic seizures.
Ordinal patterns in EEG are a potential characteristic of brain dynamics.
This simple and fast method shows promise for automated seizure prevention systems in absence epilepsy.