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Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces

Author

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  • Rui Zhang
  • Peng Xu
  • Lanjin Guo
  • Yangsong Zhang
  • Peiyang Li
  • Dezhong Yao

Abstract

Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.

Suggested Citation

  • Rui Zhang & Peng Xu & Lanjin Guo & Yangsong Zhang & Peiyang Li & Dezhong Yao, 2013. "Z-Score Linear Discriminant Analysis for EEG Based Brain-Computer Interfaces," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
  • Handle: RePEc:plo:pone00:0074433
    DOI: 10.1371/journal.pone.0074433
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    Cited by:

    1. Chun-Na Li & Pei-Wei Ren & Yan-Ru Guo & Ya-Fen Ye & Yuan-Hai Shao, 2024. "Regularized linear discriminant analysis based on generalized capped $$l_{2,q}$$ l 2 , q -norm," Annals of Operations Research, Springer, vol. 339(3), pages 1433-1459, August.
    2. Ke Yu & Hasan AI-Nashash & Nitish Thakor & Xiaoping Li, 2014. "The Analytic Bilinear Discrimination of Single-Trial EEG Signals in Rapid Image Triage," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.

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