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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 82069, 10 pages
http://dx.doi.org/10.1155/2007/82069
Research Article

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

1Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Gartenstr. 29, Tübingen 72074, Germany
2Wilhelm-Schickard Institute for Computer Engineering, University of Tübingen, Sand 13, Tübingen 72076, Germany
3National Institutes of Health (NIH), National Institute of Neurological Disorders and Stroke (NINDS), Human Cortical Physiology Section, Bethesda, MD 20892, USA
4Computer Engineering, Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Leipzig, Johannisgasse 26, Leipzig 04103, Germany

Received 16 February 2007; Revised 31 May 2007; Accepted 23 August 2007

Academic Editor: Andrzej Cichocki

Copyright © 2007 Sebastian Halder 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

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.