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Comparison of complex-valued independent component analysis algorithms for EEG data
Author
Source
The Iraqi Journal of Electrical and Electronic Engineering
Issue
Vol. 15, Issue 1 (30 Jun. 2019), pp.1-12, 12 p.
Publisher
University of Basrah College of Engineering
Publication Date
2019-06-30
Country of Publication
Iraq
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Abstract EN
Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain.
In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements.
However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation.
Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources.
However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state.
The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm.
In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data.
Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known.
We address this by developing proper measures to compare the results from multiple algorithms.
The comparisons consider the ability of an algorithm to find interesting independent sources, i.e.
those related to brain activity and not to artifact activity.
The performance of locating a dipole for each separated independent component is considered in the comparison as well.
Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties.
This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain
American Psychological Association (APA)
al-Sayigh, Ali. 2019. Comparison of complex-valued independent component analysis algorithms for EEG data. The Iraqi Journal of Electrical and Electronic Engineering،Vol. 15, no. 1, pp.1-12.
https://search.emarefa.net/detail/BIM-892137
Modern Language Association (MLA)
al-Sayigh, Ali. Comparison of complex-valued independent component analysis algorithms for EEG data. The Iraqi Journal of Electrical and Electronic Engineering Vol. 15, no. 1 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-892137
American Medical Association (AMA)
al-Sayigh, Ali. Comparison of complex-valued independent component analysis algorithms for EEG data. The Iraqi Journal of Electrical and Electronic Engineering. 2019. Vol. 15, no. 1, pp.1-12.
https://search.emarefa.net/detail/BIM-892137
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 12
Record ID
BIM-892137