Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems
Joint Authors
Yao, Dezhong
Zhang, Rui
Gao, Dongrui
Li, Fali
Ma, Teng
Lv, Xulin
Li, Peiyang
Xu, Peng
Liu, Tiejun
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-13
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gao, Dongrui& Zhang, Rui& Liu, Tiejun& Li, Fali& Ma, Teng& Lv, Xulin…[et al.]. 2015. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems. Computational and Mathematical Methods in Medicine،Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057970
Modern Language Association (MLA)
Gao, Dongrui…[et al.]. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems. Computational and Mathematical Methods in Medicine No. 2015 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1057970
American Medical Association (AMA)
Gao, Dongrui& Zhang, Rui& Liu, Tiejun& Li, Fali& Ma, Teng& Lv, Xulin…[et al.]. Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems. Computational and Mathematical Methods in Medicine. 2015. Vol. 2015, no. 2015, pp.1-7.
https://search.emarefa.net/detail/BIM-1057970
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1057970