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Stationary common spatial patterns: Towards robust classification of non-stationary EEG signals
Citation key WojICASSP11
Author Wojcikiewicz, W. and Vidaurre, C. and Kawanabe, M.
Title of Book Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Pages 577-580
Year 2011
ISBN 978-1-4577-0538-0
ISSN 1520-6149
DOI 10.1109/ICASSP.2011.5946469
Month May
Abstract Brain-Computer Interfaces (BCIs) allow a user to control a computer application by brain activity as acquired, e.g., by EEG. A standard step in a BCI system is to project the EEG signals to a low-dimensional subspace using Common Spatial Patterns (CSP). However, non-stationarities in the data can negatively affect the performance of CSP, i.e. variation of the signal properties within and across experimental sessions coming from electrode artefacts, alpha or muscular activity, or fatigue may result in suboptimal projection directions. We alleviate this problem by regularizing CSP towards stationary subspaces and show that this especially increases classification accuracy of people who are not able to control a BCI i.e. have more than 30% of error. These users very often show non-stationarities in their EEG signals.
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