Networking Property During Epileptic Seizure with Multi-channel EEG Recordings

  • Huihua Wu
  • Xiaoli Li
  • Xinping Guan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


EEG recordings are widely used in epilepsy research. We intend to address a question whether small world network property exists in neural networks when epileptic seizures occur. In this paper, we introduce a bispectrum analysis to calculate the interaction between two EEG recordings; then, a suitable threshold is chosen to convert the interaction of the six channels at five frequency bands to a sparse graph (node: n=30, edge: k=4-7). Through analyzing a real EEG recording, it is found the clustering coefficient is similar to that of regular graph; however the path length is less than that of regular graph. Thus a primary suggestion can be made that neural networks possess small world network characteristic. During epileptic seizures, the small world property of neural network is more significant.


Random Graph Epileptic Seizure Cluster Coefficient Regular Graph Small World 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huihua Wu
    • 1
  • Xiaoli Li
    • 1
  • Xinping Guan
    • 1
  1. 1.Institute of Electrical EngineeringYanshan UniversityQinhuangdaoChina

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