PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG
Joint Authors
Ball, Kenneth
Bigdely-Shamlo, Nima
Robbins, Kay
Mullen, Tim
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-02
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data.
Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations.
We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints.
The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space.
We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data.
On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA.
On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods.
In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.
American Psychological Association (APA)
Ball, Kenneth& Bigdely-Shamlo, Nima& Mullen, Tim& Robbins, Kay. 2016. PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-20.
https://search.emarefa.net/detail/BIM-1099827
Modern Language Association (MLA)
Ball, Kenneth…[et al.]. PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-20.
https://search.emarefa.net/detail/BIM-1099827
American Medical Association (AMA)
Ball, Kenneth& Bigdely-Shamlo, Nima& Mullen, Tim& Robbins, Kay. PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-20.
https://search.emarefa.net/detail/BIM-1099827
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099827