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Abstract:
Designing a Brain Computer Interface (BCI) system one can choose from a variety of
features that may be useful for classifying brain activity during a mental task.
For the special case of classifying EEG signals we propose the usage of the state
of the art feature selection algorithms Recursive Feature Elimination and Zero-Norm Optimization
which are based on the training of Support Vector Machines (SVM).
These algorithms can provide more accurate solutions than standard filter methods for feature selection.
We adapt the methods for the purpose of selecting EEG channels.
For a motor imagery paradigm we show that the number of used channels can be
reduced significantly without increasing the
classification error.
The resulting best channels agree well with the expected underlying cortical activity patterns during the mental tasks.
Furthermore we show how time dependent task specific information can be visualized.
