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Coarse-to-Fine Classification with Phase Synchronization and Common Spatial Pattern for Motor Imagery-Based BCI

  • Wenfen Ling
  • Feipeng Xu
  • Qiaonan Fan
  • Yong Peng
  • Wanzeng KongEmail author
Conference paper
  • 30 Downloads
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 581)

Abstract

How to improve the classification accuracy is a key issue in four-class motor imagery-based brain-computer interface (MI-BCI) systems. In this paper, a method based on phase synchronization analysis and common spatial pattern (CSP) algorithm is proposed. The proposed method embodies the idea of the inverted binary tree, which transforms the multi-class problem into several binary problems. First, the phase locking value (PLV) is calculated as a feature of phase synchronization, then the calculated correlation coefficients of the phase synchronization features are used to construct two pairs of class. Subsequently, we use CSP to extract the features of each class pair and use the linear discriminant analysis (LDA) to classify the test samples and obtain coarse classification results. Finally, the two classes obtained from the coarse classification form a new class pair. We use CSP and LDA to classify the test samples and get the fine classification results. The performance of method is evaluated on BCI Competition IV dataset IIa. The average kappa coefficient of our method is ranked third among the experimental results of the first five contestants. In addition, the classification performance of several subjects is significantly improved. These results show this method is effective for multi-class motor imagery classification.

Keywords

Electroencephalography Motor imagery Phase synchronization Common spatial pattern Linear discriminant analysis 

Notes

Acknowledgment

This work was supported by National Key R&D Program of China for Intergovernmental International Science and Technology Innovation Cooperation Project (2017YFE0116800), National Natural Science Foundation of China (61671193, U1909202), Science and Technology Program of Zhejiang Province (2018C04012), Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province (2020E10010) and the Graduate Scientific Research Foundation of Hangzhou Dianzi University (CXJJ2019121).

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Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  • Wenfen Ling
    • 1
    • 2
  • Feipeng Xu
    • 1
    • 2
  • Qiaonan Fan
    • 1
    • 2
  • Yong Peng
    • 1
    • 2
  • Wanzeng Kong
    • 1
    • 2
    Email author
  1. 1.College of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  2. 2.Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang ProvinceHangzhouChina

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