PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stationary Common Spatial Patterns: Towards Robust Classification of Non-Stationary EEG Signals
Wojciech Wojcikiewicz, Carmen Vidaurre and Motoaki Kawanabe
In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague(2011).


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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:9440
Deposited By:Wojciech Samek
Deposited On:16 March 2012