An evidence-based combining classifier for brain signal analysis.

Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal anal...

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Autores principales:	Saeed Reza Kheradpisheh, Abbas Nowzari-Dalini, Reza Ebrahimpour, Mohammad Ganjtabesh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/890cfc6f649641549555d341ef2d180a
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spelling	oai:doaj.org-article:890cfc6f649641549555d341ef2d180a2021-11-18T08:39:06ZAn evidence-based combining classifier for brain signal analysis.1932-620310.1371/journal.pone.0084341https://doaj.org/article/890cfc6f649641549555d341ef2d180a2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24392125/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.Saeed Reza KheradpishehAbbas Nowzari-DaliniReza EbrahimpourMohammad GanjtabeshPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e84341 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saeed Reza Kheradpisheh Abbas Nowzari-Dalini Reza Ebrahimpour Mohammad Ganjtabesh An evidence-based combining classifier for brain signal analysis.
description	Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. Hence, the need for robust signal analysis methods with adequate accuracy and generalizability is inevitable. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. Moreover, because of the non-stationarity of brain signals and the impacts of mental states on brain function, the brain signals are associated with an inherent uncertainty. In this paper, an evidence-based combining classifiers method is proposed for brain signal analysis. This method exploits the power of combining classifiers for solving complex problems and the ability of evidence theory to model as well as to reduce the existing uncertainty. The proposed method models the uncertainty in the labels of training samples in each feature space by assigning soft and crisp labels to them. Then, some classifiers are employed to approximate the belief function corresponding to each feature space. By combining the evidence raised from each classifier through the evidence theory, more confident decisions about testing samples can be made. The obtained results by the proposed method compared to some other evidence-based and fixed rule combining methods on artificial and real datasets exhibit the ability of the proposed method in dealing with complex and uncertain classification problems.
format	article
author	Saeed Reza Kheradpisheh Abbas Nowzari-Dalini Reza Ebrahimpour Mohammad Ganjtabesh
author_facet	Saeed Reza Kheradpisheh Abbas Nowzari-Dalini Reza Ebrahimpour Mohammad Ganjtabesh
author_sort	Saeed Reza Kheradpisheh
title	An evidence-based combining classifier for brain signal analysis.
title_short	An evidence-based combining classifier for brain signal analysis.
title_full	An evidence-based combining classifier for brain signal analysis.
title_fullStr	An evidence-based combining classifier for brain signal analysis.
title_full_unstemmed	An evidence-based combining classifier for brain signal analysis.
title_sort	evidence-based combining classifier for brain signal analysis.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/890cfc6f649641549555d341ef2d180a
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An evidence-based combining classifier for brain signal analysis.

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