Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes†or “no†or require many weeks of biofeedback training. We hypothesized that signal processing and machine...

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Bibliographic Details
Main Authors: Michael H. Thaut, Charles W. Anderson, Michael J. Kirby, James N. Knight, David A. Peterson
Format: Article
Language:English
Published: SpringerOpen 2005-11-01
Series:EURASIP Journal on Advances in Signal Processing
Subjects:
electroencephalogram
brain-computer interface
feature selection
independent components analysis
support vector machine
genetic algorithm.
Online Access:http://dx.doi.org/10.1155/ASP.2005.3128

Internet

http://dx.doi.org/10.1155/ASP.2005.3128

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