title: An enhanced probabilistic LDA for multi-class brain computer interface.
abstract: BACKGROUND
  There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results.
  
  
  RESULTS
  In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA) for multi-class motor imagery BCI, which utilized Bayesian linear discriminant analysis (BLDA) with probabilistic output to improve the classification performance. EBLDA builds a new classifier that enlarges training dataset by adding test samples with high probability. EBLDA is based on the hypothesis that unlabeled samples with high probability provide valuable information to enhance learning process and generate a classifier with refined decision boundaries. To investigate the performance of EBLDA, we first used carefully designed simulated datasets to study how EBLDA works. Then, we adopted a real BCI dataset for further evaluation. The current study shows that: 1) Probabilistic information can improve the performance of BCI for subjects with high kappa coefficient; 2) With supplementary training samples from the test samples of high probability, EBLDA is significantly better than BLDA in classification, especially for small training datasets, in which EBLDA can obtain a refined decision boundary by a shift of BLDA decision boundary with the support of the information from test samples.
  
  
  CONCLUSIONS
  The proposed EBLDA could potentially reduce training effort. Therefore, it is valuable for us to realize an effective online BCI system, especially for multi-class BCI systems.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China. xupeng@uestc.edu.cn
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
authors.forename: Ping
authors.initials: P
authors.lastname: Yang
authors.forename: Xu
authors.initials: X
authors.lastname: Lei
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
mesh_terms: D001185:Artificial Intelligence; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003199:Computer Systems; D006801:Humans; D011336:Probability; D014584:User-Computer Interface