Open AccessBMICA - Independent Component Analysis Based on B-Spline Mutual Information Estimator
Author(s) -
Janett Walters Williams
Publication year - 2012
Publication title -
signal and image processing : an international journal
Language(s) - English
Resource type - Journals
eISSN - 2229-3922
pISSN - 0976-710X
DOI - 10.5121/sipij.2012.3203
Subject(s) - b spline , estimator , mutual information , independent component analysis , mathematics , component (thermodynamics) , statistics , computer science , artificial intelligence , mathematical analysis , physics , thermodynamics
The information theoretic concept of mutual information provides a general framework to evaluate dependencies between variables. Its estimation however using B-Spline has not been used before in creating an approach for Independent Component Analysis. In this paper we present a B-Spline estimator for mutual information to find the independent components in mixed signals. Tested using electroencephalography (EEG) signals the resulting BMICA (B-Spline Mutual Information Independent Component Analysis)
exhibits better performance than the standard Independent Component Analysis algorithms of FastICA, JADE, SOBI and EFICA in similar simulations. BMICA was found to be also more reliable than the 'renown' FastICA.