Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data
Joint Authors
Brunner, Clemens
Naim, Muhammad
Pfurtscheller, Gert
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-06-08
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Naim, Muhammad& Brunner, Clemens& Pfurtscheller, Gert. 2009. Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data. Computational Intelligence and Neuroscience،Vol. 2009, no. 2009, pp.1-8.
https://search.emarefa.net/detail/BIM-479625
Modern Language Association (MLA)
Naim, Muhammad…[et al.]. Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data. Computational Intelligence and Neuroscience No. 2009 (2009), pp.1-8.
https://search.emarefa.net/detail/BIM-479625
American Medical Association (AMA)
Naim, Muhammad& Brunner, Clemens& Pfurtscheller, Gert. Dimensionality Reduction and Channel Selection of Motor Imagery Electroencephalographic Data. Computational Intelligence and Neuroscience. 2009. Vol. 2009, no. 2009, pp.1-8.
https://search.emarefa.net/detail/BIM-479625
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-479625