| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 411030 | Neurocomputing | 2006 | 5 Pages |
Abstract
This paper presents a new global optimal independent component analysis (ICA) algorithm. The constrained optimization problem, which is typically encountered with conventional ICA, is converted to an unconstrained problem with the orthogonal projection approach. Then the particle swarm optimization (PSO) is employed to solve the unconstrained problem and determine the separating matrix of ICA. Applications in the analysis of the magnetoencephalographic recordings (MEG) illustrate the efficiency of the proposed PSO–ICA approach.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lei Xie, Jun Wu,