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Unique estimation in EEG analysis by the ordering ICA

Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional I...

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Detalles Bibliográficos
Autores principales: Matsuda, Yoshitatsu, Yamaguchi, Kazunori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9591063/
https://www.ncbi.nlm.nih.gov/pubmed/36279275
http://dx.doi.org/10.1371/journal.pone.0276680
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author Matsuda, Yoshitatsu
Yamaguchi, Kazunori
author_facet Matsuda, Yoshitatsu
Yamaguchi, Kazunori
author_sort Matsuda, Yoshitatsu
collection PubMed
description Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional ICA algorithms is that their results often fluctuate and do not converge to the unique and globally optimal solution at each run. It is because there are many local optima and permutation ambiguities. We have recently proposed a new ICA algorithm named the ordering ICA, a simple extension of Fast ICA. The ordering ICA is theoretically guaranteed to extract the independent components in the unique order and avoids the local optima in practice. This paper investigated the usefulness of the ordering ICA in EEG analysis. Experiments showed that the ordering ICA could give unique solutions for the signals with large non-Gaussianity, and the ease of parallelization could reduce computation time.
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spelling pubmed-95910632022-10-25 Unique estimation in EEG analysis by the ordering ICA Matsuda, Yoshitatsu Yamaguchi, Kazunori PLoS One Research Article Independent Component Analysis (ICA) is a method for solving blind source separation problems. Because ICA only needs weak assumptions to estimate the unknown sources from only the observed signals, it is suitable for Electroencephalography (EEG) analysis. A serious disadvantage of the traditional ICA algorithms is that their results often fluctuate and do not converge to the unique and globally optimal solution at each run. It is because there are many local optima and permutation ambiguities. We have recently proposed a new ICA algorithm named the ordering ICA, a simple extension of Fast ICA. The ordering ICA is theoretically guaranteed to extract the independent components in the unique order and avoids the local optima in practice. This paper investigated the usefulness of the ordering ICA in EEG analysis. Experiments showed that the ordering ICA could give unique solutions for the signals with large non-Gaussianity, and the ease of parallelization could reduce computation time. Public Library of Science 2022-10-24 /pmc/articles/PMC9591063/ /pubmed/36279275 http://dx.doi.org/10.1371/journal.pone.0276680 Text en © 2022 Matsuda, Yamaguchi https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Matsuda, Yoshitatsu
Yamaguchi, Kazunori
Unique estimation in EEG analysis by the ordering ICA
title Unique estimation in EEG analysis by the ordering ICA
title_full Unique estimation in EEG analysis by the ordering ICA
title_fullStr Unique estimation in EEG analysis by the ordering ICA
title_full_unstemmed Unique estimation in EEG analysis by the ordering ICA
title_short Unique estimation in EEG analysis by the ordering ICA
title_sort unique estimation in eeg analysis by the ordering ica
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9591063/
https://www.ncbi.nlm.nih.gov/pubmed/36279275
http://dx.doi.org/10.1371/journal.pone.0276680
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