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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9591063 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT matsudayoshitatsu uniqueestimationineeganalysisbytheorderingica AT yamaguchikazunori uniqueestimationineeganalysisbytheorderingica |