Dimensionality reduction and channel selection of motor imagery electroencephalographic data

Authors:
Muhammad Naeem;Clemens Brunner;Gert Pfurtscheller
Affiliations:
BCI Lab, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria;BCI Lab, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria;BCI Lab, Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria
Venue:
Computational Intelligence and Neuroscience - Neuromath: advanced methods for the estimation of human brain activity and connectivity
Year:
2009

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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 centroparietal regions of the brain revealed that common spatial patterns (CSPs) and Infomax were able to detect motor imagery activity with a satisfactory accuracy.