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Dynamical Similarity Analysis of EEG Recordings

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Abstract

Electroencephalogram (EEG) recordings contain a large amount of information about physiological and pathological processes in the brain and serve as one of the important tools in clinical diagnosis and research regarding epilepsy. Dynamical similarity analysis is applied to characterize EEG changes in different absence seizure states. The average similarity measure of a pair of EEG signals in the same seizure states and across different seizure states is calculated using an improved dynamical similarity method. The results show that the average similarity measures between EEG segments within the seizure-free state are close to 1, suggesting that the EEG segments within the seizure-free state share the same dynamic characteristics. The similarity measures between EEG segments across different seizure states are typically smaller, indicating that the changes of dynamic characteristics can be found during different absence seizure states.

Keywords

EEG Dynamical similarity Seizure states 

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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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