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Stationary Common Spatial Patterns: Towards Robust Classification of Non-Stationary EEG Signals.
Zitatschlüssel WojcikiewiczW.2011
Autor Wojcikiewicz, W., Vidaurre, C., Kawanabe, M.
Buchtitel Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference
Seiten 577-580
Jahr 2011
ISBN 1520-6149
DOI 10.1109/ICASSP.2011.5946469
Ort Prague
Monat May
Zusammenfassung 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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