title: Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems.
abstract: BACKGROUND
  Usually the training set of online brain-computer interface (BCI) experiment is small. For the small training set, it lacks enough information to deeply train the classifier, resulting in the poor classification performance during online testing.
  
  
  METHODS
  In this paper, on the basis of Z-LDA, we further calculate the classification probability of Z-LDA and then use it to select the reliable samples from the testing set to enlarge the training set, aiming to mine the additional information from testing set to adjust the biased classification boundary obtained from the small training set. The proposed approach is an extension of previous Z-LDA and is named enhanced Z-LDA (EZ-LDA).
  
  
  RESULTS
  We evaluated the classification performance of LDA, Z-LDA, and EZ-LDA on simulation and real BCI datasets with different sizes of training samples, and classification results showed EZ-LDA achieved the best classification performance.
  
  
  CONCLUSIONS
  EZ-LDA is promising to deal with the small sample size training problem usually existing in online BCI system.
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Dongrui
authors.initials: D
authors.lastname: Gao
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China ; Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Liu
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Fali
authors.initials: F
authors.lastname: Li
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Teng
authors.initials: T
authors.lastname: Ma
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Xulin
authors.initials: X
authors.lastname: Lv
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Peiyang
authors.initials: P
authors.lastname: Li
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China ; Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China ; Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
mesh_terms: D001499:Bayes Theorem; D062207:Brain-Computer Interfaces / Q000706:statistics & numerical data*; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000706:statistics & numerical data; D005074:Evoked Potentials, Visual; D005260:Female; D006801:Humans; D016014:Linear Models; D000069550:Machine Learning; D008297:Male; D009862:Online Systems; D011336:Probability; D018401:Sample Size; D055815:Young Adult