Support vector channel selection in BCI.
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 electroencephalogram (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.
Lal
Thomas Navin
TN
Max-Planck-Institut for Biological Cybernetics, Spemannstr. 38, Tübingen 72076, Germany. navin@tuebingen.mpg.de
Schröder
Michael
M
Hinterberger
Thilo
T
Weston
Jason
J
Bogdan
Martin
M
Birbaumer
Niels
N
Schölkopf
Bernhard
B
D000465:Algorithms; D001185:Artificial Intelligence; D002540:Cerebral Cortex / Q000502:physiology*; D016000:Cluster Analysis; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006225:Hand / Q000502:physiology; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface