title: Dimensionality reduction and channel selection of motor imagery electroencephalographic data.
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.
authors.affiliation: BCI Lab, Institute for Knowledge Discovery, Graz University of Technology, 8010 Graz, Austria.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Naeem
authors.forename: Clemens
authors.initials: C
authors.lastname: Brunner
authors.forename: Gert
authors.initials: G
authors.lastname: Pfurtscheller