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Order Time Series Analysis of Neural Signals

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Abstract

Order time series analysis, a very simple and fast algorithm, is used to track the transient dynamics of electroencephalogram (EEG) recordings during the different absence seizure states in absence seizure epileptic rats. Permutation entropy, forbidden order patterns (FOP), and dissimilarity index are applied to analyze the EEG data from the seizure-free, the pre-seizure, and seizure phases. The results show that the number of FOPs in pre-seizure EEG epochs is higher than that in seizure-free EEG epochs but lower than that in seizure EEG epochs. Furthermore, we investigate order time series analysis as a tool to detect the pre-seizure state by using EEG recordings. The results show that order time series analysis can track the dynamical changes of EEG data so as to describe transient dynamics prior to the absence seizures.

Keywords

EEG Forbidden order patterns Dissimilarity index Permutation entropy Absence seizures 

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  2. 2.Center for Collaboration and Innovation in Brain and Learning SciencesBeijing Normal UniversityBeijingChina

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