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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 680769, 7 pages
http://dx.doi.org/10.1155/2015/680769
Research Article

Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems

1Key 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
2Center for Information in Bio-Medicine, University of Electronic Science and Technology of China, Chengdu 611731, China

Received 10 July 2015; Revised 28 September 2015; Accepted 28 September 2015

Academic Editor: Irena Cosic

Copyright © 2015 Dongrui Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.