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EEG Signal Analysis and Classification pp 153–172Cite as

Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications

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Part of the book series: Health Information Science ((HIS))

Abstract

One crucial and challenging issue in BCI systems is the identification of motor imagery (MI) task based EEG signals in the biomedical engineering research area. Although BCI techniques have been developing quickly in recent decades, there remains a number of unsolved problems such as the improvement of MI signal classification.This chapter proposes a new approach, the ‘Cross-correlation aided logistic regression model’ called “CC-LR” for efficient identification of MI tasks.

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Authors and Affiliations

  1. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Victoria, Australia

    Siuly Siuly

  2. School of Agricultural, Computational and Environmental Sciences, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Toowoomba, Queensland, QLD 4350, Australia

    Yan Li

  3. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

  4. School of Computer Science, Fudan University, Shanghai, China

    Yanchun Zhang

Authors
  1. Siuly Siuly
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  2. Yan Li
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Correspondence to Siuly Siuly .

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Siuly, S., Li, Y., Zhang, Y. (2016). Cross-Correlation Aided Logistic Regression Model for the Identification of Motor Imagery EEG Signals in BCI Applications. In: EEG Signal Analysis and Classification. Health Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-47653-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-47653-7_8

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