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Title: Evolutionary based ICA with reference for EEG μ rhythm extraction
Authors: Kavuri, Swathi Sri
Veluvolu, Kalyana Chakravarthy
Chai, Quek Hiok
Keywords: Constrained ICA
Differential Evolution
Issue Date: 2018
Source: Kavuri, S. S., Veluvolu, K. C., & Chai, Q. H. (2018). Evolutionary based ICA with reference for EEG μ rhythm extraction. IEEE Access, 6, 19702-19713.
Series/Report no.: IEEE Access
Abstract: Independent component analysis with reference (ICA-R), a paradigm of constrained ICA (cICA), incorporates textita priori information about the desired sources as reference signals into the contrast function of ICA. Reference signals direct the search toward the separation of desired sources more efficiently and accurately than the ICA. The penalized contrast function of ICA-R is non-smooth everywhere and the ICA-R algorithm does not always reach the global optimum due to the Newton-like learning used. In this paper, we propose a constrained differential evolutionary algorithm with an improved initialization strategy to solve the constrained optimization problem of ICA-R that can asymptotically converge to the optimum. It completely avoids the formulation of a penalized contrast function and scaling (due to the Lagrangian multipliers) by incorporating the ICA contrast function and the violation of the closeness constraint into the selection process of the evolution. Experiments with synthetic data and isolation of μ rhythmic activity from EEG showed improved source extraction performance over ICA-R and its recent enhancements.
DOI: 10.1109/ACCESS.2018.2821838
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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