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The Feature Extraction Method of EEG Signals Based on the Loop Coefficient of Transition Network

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

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Abstract

High accuracy of epilepsy EEG automatic detection has important clinical research significance. The combination of nonlinear time series analysis and complex network theory made it possible to analyze time series by the statistical characteristics of complex network. In this paper, based on the transition network the feature extraction method of EEG signals was proposed. Based on the complex network, the epileptic EEG data were transformed into the transition network, and the loop coefficient was extracted as the feature to classify the epileptic EEG signals. Experimental results show that the single feature classification based on the extracted feature obtains classification accuracy up to 98.5%, which indicates that the classification accuracy of the single feature based on the transition network was very high.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61671220, 61640218, 61201428), the National Key Research And Development Plan (No. 2016 YFC0106001), the Shandong Distinguished Middle aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016 GGX101022).

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Authors and Affiliations

  1. School of Information Science and Engineering, University of Jinan, Jinan, 250022, China

    Mingmin Liu, Qingfang Meng, Hanyong Zhang & Dong Wang

  2. Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China

    Mingmin Liu, Qingfang Meng, Hanyong Zhang & Dong Wang

  3. Institute of Jinan Semoconductor Elements Experimentation, Jinan, 250014, China

    Qiang Zhang

Authors
  1. Mingmin Liu
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  2. Qingfang Meng
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  3. Qiang Zhang
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  4. Hanyong Zhang
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  5. Dong Wang
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Corresponding author

Correspondence to Qingfang Meng .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Liu, M., Meng, Q., Zhang, Q., Zhang, H., Wang, D. (2017). The Feature Extraction Method of EEG Signals Based on the Loop Coefficient of Transition Network. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_63

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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