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Multi-scale symbolic transfer entropy analysis of EEG

Author

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  • Yao, Wenpo
  • Wang, Jun

Abstract

From both global and local perspectives, we symbolize two kinds of EEG and analyze their dynamic and asymmetrical information using multi-scale transfer entropy. Multi-scale process with scale factor from 1 to 199 and step size of 2 is applied to EEG of healthy people and epileptic patients, and then the permutation with embedding dimension of 3 and global approach are used to symbolize the sequences. The forward and reverse symbol sequences are taken as the inputs of transfer entropy. Scale factor intervals of permutation and global way are (37, 57) and (65, 85) where the two kinds of EEG have satisfied entropy distinctions. When scale factor is 67, transfer entropy of the healthy and epileptic subjects of permutation, 0.1137 and 0.1028, have biggest difference. And the corresponding values of the global symbolization is 0.0641 and 0.0601 which lies in the scale factor of 165. Research results show that permutation which takes contribution of local information has better distinction and is more effectively applied to our multi-scale transfer entropy analysis of EEG.

Suggested Citation

  • Yao, Wenpo & Wang, Jun, 2017. "Multi-scale symbolic transfer entropy analysis of EEG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 276-281.
    • Handle: RePEc:eee:phsmap:v:484:y:2017:i:c:p:276-281
    DOI: 10.1016/j.physa.2017.04.181
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    Cited by:

    1. Kuang, Peng-Cheng, 2021. "Measuring information flow among international stock markets: An approach of entropy-based networks on multi time-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 577(C).

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