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...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2022-10, Vol.17 (10), p.e0276680-e0276680
Main Authors: Matsuda, Yoshitatsu, Yamaguchi, Kazunori
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and Life Sciences
EEG
Electroencephalography
Estimates
Health aspects
Independent component analysis
Kurtosis
Medicine and Health Sciences
Methods
Optimization
Parallel processing
Permutations
Physical Sciences
Principal components analysis
Research and Analysis Methods
Signal processing
Uniqueness
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0276680