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Computational Intelligence and Neuroscience
Volume 2009, Article ID 537504, 8 pages
http://dx.doi.org/10.1155/2009/537504
Research Article

Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data

BCI Lab, Institute for Knowledge Discovery, Graz University of Technology, 8010 Graz, Austria

Received 27 October 2008; Revised 19 January 2009; Accepted 24 March 2009

Academic Editor: Fabio Babiloni

Copyright © 2009 Muhammad Naeem et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The performance of spatial filters based on independent components analysis (ICA) was evaluated by employing principal component analysis (PCA) preprocessing for dimensional reduction. The PCA preprocessing was not found to be a suitable method that could retain motor imagery information in a smaller set of components. In contrast, 6 ICA components selected on the basis of visual inspection performed comparably (61.9%) to the full range of 22 components (63.9%). An automated selection of ICA components based on a variance criterion was also carried out. Only 8 components chosen this way performed better (63.1%) than visually selected components. A similar analysis on the reduced set of electrodes over mid-central and centro-parietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.