By Topic

Empirical mode decomposition to approach the problem of detecting sources from a reduced number of mixtures

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Balocchi, R. ; Inst. of Clinical Physiol., Nat. Res. Council, Pisa, Italy ; Menicucci, D. ; Varanini, M.

The paper presents a new approach of Blind Source Separation based on the combined use of Empirical Mode Decomposition (EMD) and Factor Analysis (FA) for the case of more sources than observable signals, the so called overcomplete problem. The EMD-FA performance is tested both over artificial data and real EEG signals and compared with that of the more traditional Independent Component Analysis (ICA). The EMD-FA approach exhibited a neatly superior performance in the overcomplete problem with respect to traditional ICA. Furthermore this approach can be adopted even for nonlinear and nonstationary signals, which makes it very attractive for biomedical signal processing.

Published in:

Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE  (Volume:3 )

Date of Conference:

17-21 Sept. 2003