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

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ID: 56046
2007
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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.
Reference Key
halder2007onlinecomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Halder, Sebastian;Bensch, Michael;Mellinger, Jürgen;Bogdan, Martin;Kübler, Andrea;Birbaumer, Niels;Rosenstiel, Wolfgang;
Journal Computational Intelligence and Neuroscience
Year 2007
DOI
DOI not found
URL
http://dx.doi.org/10.1155/2007/82069
Keywords
Engineering (General). Civil engineering (General) Computer applications to medicine. Medical informatics neurosciences. biological psychiatry. neuropsychiatry mathematics

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