Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems
In: Computational and Mathematical Methods in Medicine, Vol 2015 (2015, 2015
Online
academicJournal
Zugriff:
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.
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Enhanced Z-LDA for Small Sample Size Training in Brain-Computer Interface Systems
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Autor/in / Beteiligte Person: | Gao, Dongrui ; Zhang, Rui ; Liu, Tiejun ; Li, Fali ; Ma, Teng ; Lv, Xulin ; Li, Peiyang ; Yao, Dezhong ; Xu, Peng |
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Zeitschrift: | Computational and Mathematical Methods in Medicine, Vol 2015 (2015, 2015 |
Veröffentlichung: | Hindawi Limited, 2015 |
Medientyp: | academicJournal |
ISSN: | 1748-670X (print) ; 1748-6718 (print) |
DOI: | 10.1155/2015/680769 |
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