Z-score linear discriminant analysis for EEG based brain-computer interfaces.

Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications...

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Autores principales:	Rui Zhang, Peng Xu, Lanjin Guo, Yangsong Zhang, Peiyang Li, Dezhong Yao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e72d4fc0ff894a55812c19a15fc275c6
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id	oai:doaj.org-article:e72d4fc0ff894a55812c19a15fc275c6
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spelling	oai:doaj.org-article:e72d4fc0ff894a55812c19a15fc275c62021-11-18T08:55:19ZZ-score linear discriminant analysis for EEG based brain-computer interfaces.1932-620310.1371/journal.pone.0074433https://doaj.org/article/e72d4fc0ff894a55812c19a15fc275c62013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24058565/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.Rui ZhangPeng XuLanjin GuoYangsong ZhangPeiyang LiDezhong YaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e74433 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rui Zhang Peng Xu Lanjin Guo Yangsong Zhang Peiyang Li Dezhong Yao Z-score linear discriminant analysis for EEG based brain-computer interfaces.
description	Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.
format	article
author	Rui Zhang Peng Xu Lanjin Guo Yangsong Zhang Peiyang Li Dezhong Yao
author_facet	Rui Zhang Peng Xu Lanjin Guo Yangsong Zhang Peiyang Li Dezhong Yao
author_sort	Rui Zhang
title	Z-score linear discriminant analysis for EEG based brain-computer interfaces.
title_short	Z-score linear discriminant analysis for EEG based brain-computer interfaces.
title_full	Z-score linear discriminant analysis for EEG based brain-computer interfaces.
title_fullStr	Z-score linear discriminant analysis for EEG based brain-computer interfaces.
title_full_unstemmed	Z-score linear discriminant analysis for EEG based brain-computer interfaces.
title_sort	z-score linear discriminant analysis for eeg based brain-computer interfaces.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e72d4fc0ff894a55812c19a15fc275c6
work_keys_str_mv	AT ruizhang zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces AT pengxu zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces AT lanjinguo zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces AT yangsongzhang zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces AT peiyangli zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces AT dezhongyao zscorelineardiscriminantanalysisforeegbasedbraincomputerinterfaces
_version_	1718421174163079168

Z-score linear discriminant analysis for EEG based brain-computer interfaces.

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