Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

Journal: Journal of neuroscience methods
Published Date: Jul 17, 2015

Abstract

BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation.

Authors

  • Jun Shi
    School of Communication and Information Engineering, Shanghai University, Shanghai, China. Electronic address: junshi@staff.shu.edu.cn.
  • Xiao Liu
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yingjie Li
    School of Communication and Information Engineering, Shanghai University, China.
  • Shihui Ying
    Department of Mathematics, School of Science, Shanghai University, China. Electronic address: shying@shu.edu.cn.

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