An enhanced probabilistic LDA for multi-class brain computer interface.

<h4>Background</h4>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 contr...

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Autores principales:	Peng Xu, Ping Yang, Xu Lei, Dezhong Yao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/baaef5d235b04f7dab354daca13397a0
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spelling	oai:doaj.org-article:baaef5d235b04f7dab354daca13397a02021-11-18T06:59:33ZAn enhanced probabilistic LDA for multi-class brain computer interface.1932-620310.1371/journal.pone.0014634https://doaj.org/article/baaef5d235b04f7dab354daca13397a02011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21297944/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methodology/principal findings</h4>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.<h4>Conclusions/significance</h4>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.Peng XuPing YangXu LeiDezhong YaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 1, p e14634 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Peng Xu Ping Yang Xu Lei Dezhong Yao An enhanced probabilistic LDA for multi-class brain computer interface.
description	<h4>Background</h4>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.<h4>Methodology/principal findings</h4>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.<h4>Conclusions/significance</h4>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.
format	article
author	Peng Xu Ping Yang Xu Lei Dezhong Yao
author_facet	Peng Xu Ping Yang Xu Lei Dezhong Yao
author_sort	Peng Xu
title	An enhanced probabilistic LDA for multi-class brain computer interface.
title_short	An enhanced probabilistic LDA for multi-class brain computer interface.
title_full	An enhanced probabilistic LDA for multi-class brain computer interface.
title_fullStr	An enhanced probabilistic LDA for multi-class brain computer interface.
title_full_unstemmed	An enhanced probabilistic LDA for multi-class brain computer interface.
title_sort	enhanced probabilistic lda for multi-class brain computer interface.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/baaef5d235b04f7dab354daca13397a0
work_keys_str_mv	AT pengxu anenhancedprobabilisticldaformulticlassbraincomputerinterface AT pingyang anenhancedprobabilisticldaformulticlassbraincomputerinterface AT xulei anenhancedprobabilisticldaformulticlassbraincomputerinterface AT dezhongyao anenhancedprobabilisticldaformulticlassbraincomputerinterface AT pengxu enhancedprobabilisticldaformulticlassbraincomputerinterface AT pingyang enhancedprobabilisticldaformulticlassbraincomputerinterface AT xulei enhancedprobabilisticldaformulticlassbraincomputerinterface AT dezhongyao enhancedprobabilisticldaformulticlassbraincomputerinterface
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An enhanced probabilistic LDA for multi-class brain computer interface.

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