Training set extension for SVM ensemble in P300-speller with familiar face paradigm.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date: Jan 1, 2018

Abstract

BACKGROUND: P300-spellers are brain-computer interface (BCI)-based character input systems. Support vector machine (SVM) ensembles are trained with large-scale training sets and used as classifiers in these systems. However, the required large-scale training data necessitate a prolonged collection time for each subject, which results in data collected toward the end of the period being contaminated by the subject's fatigue.

Authors

  • Qi Li
    The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
  • Kaiyang Shi
    Department of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.
  • Ning Gao
    Department of Chemistry & Biochemistry, University of Texas at El Paso, Texas, USA.
  • Jian Li
    Fujian Key Laboratory of Traditional Chinese Veterinary Medicine and Animal Health, College of Animal Science, Fujian Agriculture and Forestry University, Fuzhou, China.
  • Ou Bai
    Department of Electrical and Computer Engineering, Florida International University, MI, USA.

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