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

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

doi:https://doi.org/10.1155/2007/82069

Computational Intelligence and Neuroscience

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Brain-Computer Interfaces: Towards Practical Implementations and Potential Applications

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Research Article | Open Access

Volume 2007 | Article ID 082069 | https://doi.org/10.1155/2007/82069

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

Sebastian Halder,¹Michael Bensch,²Jürgen Mellinger,¹Martin Bogdan,^2,4Andrea Kübler,¹Niels Birbaumer,^1,3and Wolfgang Rosenstiel²

Academic Editor: Andrzej Cichocki

Received16 Feb 2007

Revised31 May 2007

Accepted23 Aug 2007

Published12 Nov 2007

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.

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Copyright

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.

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