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The Effect of Channel Ordering Based on the Entropy Weight Graph on the MI-EEG Classification

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Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14272))

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Abstract

This study investigates the effect of changing channel ordering at the input end of EEGNet on the classification performance of deep learning algorithms, based on the entropy weight graph using phase locking value (PLV). The PLV is computed to reflect the phase synchronization relationship between different EEG channels and an adjacency matrix is constructed to obtain an undirected and non-fully connected graph using an appropriate threshold. The clustering coefficient is then calculated for different channels to determine the central node. Subsequently, the distances from the remaining channels to the central node are calculated using the entropy weight graph based on PLV, which serves as the basis for channel ordering. Additionally, the EEGNet is modified according to the characteristics of the EEG data to make it more suitable for the classification of the recorded motor imagery signals. The classification results demonstrate that channel ordering at the input end of the EEGNet can arrange signals with more synchronized phase information together, thereby enhancing the data separability and improving the classification performance effectively.

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Acknowledgments

The work has been financially supported by the Sichuan Science and Technology Program (GrantNos. 2022YFH0073, 2022YFS0021, 2023ZHCG0075 and 2023YFH0037), and 1 · 3 · 5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant Nos. ZYYC21004 and ZYJC21081).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Peng Ling, Kai Xi & Peng Chen

  2. Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, 610041, China

    Xi Yu

  3. Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China

    Xi Yu

  4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China

    Kang Li

  5. Med-X Center for Informatics, Sichuan University, Chengdu, 610041, China

    Kang Li

Authors
  1. Peng Ling
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  2. Kai Xi
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  3. Peng Chen
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  4. Xi Yu
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  5. Kang Li
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Corresponding author

Correspondence to Peng Chen .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huayong Yang

  2. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  3. Zhejiang University, Hangzhou, China

    Jun Zou

  4. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  5. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  6. Zhejiang University, Hangzhou, China

    Geng Yang

  7. Zhejiang University, Hangzhou, China

    Xiaoping Ouyang

  8. Harbin Institute of Technology, Shenzhen, China

    Zhiyong Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ling, P., Xi, K., Chen, P., Yu, X., Li, K. (2023). The Effect of Channel Ordering Based on the Entropy Weight Graph on the MI-EEG Classification. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_43

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  • DOI: https://doi.org/10.1007/978-981-99-6480-2_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6479-6

  • Online ISBN: 978-981-99-6480-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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